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Suggestion Mining from Opinionated Text of Big Social Media Data

computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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Expert Phishing Detection System

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： Cyber-security Phishing attacks Phishing URLs

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study

UVM Based Verification Framework for Deep Learning Hardware ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Randa Aboudeif Tasneem A. Awaad Mohamed AbdElsalam Yehea Ismail Electronics and Communications Engineering Department The American University in Cairo Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Siemens Digital Industries Software Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous Systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study.

关键词： Deep learning Runtime Design methodology Emulation Stability analysis Convolutional neural networks Reliability Hardware acceleration Field programmable gate arrays Testing

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Digital Platform to Empower the Self-Employment in Sri Lanka 6

Digital Platform to Empower the Self-Employment in Sri Lanka

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6th International Conference on Information Technology Research, ICITR 2021

作者： Wickramasinghe, H.C.P. Thebuwana, T.D. Wijesinghe, G.K.H.S. Dissanayake, U.N. Kodagoda, Nuwan Suriyawansa, Kushnara Faculty of Computing Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665420006

Unemployment is a huge problem around the world because a lack of job opportunities. People are unable to find the job opportunities according to their preferences and qualifications. As a solution for this, many countries are attempting to empower self-employment. Most of current world problems have been solved using modern technologies. Therefore, the development of self-employment also can be achieved through modern technology. The objective of our proposed platform, HIRELANCER, is empowering self-employment using modern technologies. HIRELANCER is bringing the consumers, service providers, and suppliers into the same platform. HIRELANCER will consist of innovative features that go beyond comparatively to other platforms such as an advanced mechanism to find best suitable service providers/suppliers for the service, handling the virtual front-desk, cost estimation for the services prior to contacting a service provider, and advanced facility to find a suitable career path for the people who are seeking career guidance. This research paper discusses how the innovative features of HIRELANCER will be beneficial for consumers, service providers, and suppliers and ultimately achieve our main objective, which is empowering self-employment in Sri Lanka. © 2021 IEEE.

关键词： Employment

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Intelligent Model for Predicting the Quality of Services Violation

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computers, Materials & Continua 2022年第5期71卷 3607-3619页

作者： Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs *** service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for *** users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and *** this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and *** paper also compares Simple-PSO and Parallel *** simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

关键词： Accountability particle swarm optimization mutant particle swarm optimization quality of service service level agreement

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Stakeholder Identification Overview and Challenges in Requirements engineering Prospective 2

Stakeholder Identification Overview and Challenges in Requir...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Elneel, Duha Awad Fakharudin, Abdul Sahli Ahmed, Elsadig Musa Kahtan, Hasan Abdullateef, Mansoor Universiti Malaysia Pahang Faculty of Computing Kuantan26300 Malaysia Multimedia University Faculty of Business Melaka Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Knowbis Solutions Consultancy Selangor Malaysia

ISBN: (纸本)9781665436052

Stakeholder identification (SI) illustrates a critical part of the requirements elicitation activity. It helps software analysts gather accurate system requirements to ensure high quality and avoid system failure. Stakeholder identification considered one of the earliest stages of project development. The main problem related to project success and failure is identifying the real stakeholders and selecting the right one. The lack of stakeholder identification methodologies and the shortage of previous research in identifying the stakeholders for different systems, encourage the research to propose a guideline framework, assist analysts in building the convenient methodology for identifying real stakeholders. Stakeholders are defined based on different projects and the various services they provide. This difference leads to the fact that a particular and systematic technique must be used to identify stakeholders based on the project's types. The exploration of stakeholder identification matters and the guideline framework is a baseline for future research in requirements engineering. © 2022 IEEE.

关键词： Requirements engineering

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption 14

Design and FPGA Implementation of the Henon Chaotic Map for ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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On the Impact of Noisy Labels on Supervised Classification Models 23rd

On the Impact of Noisy Labels on Supervised Classificatio...

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23rd International Conference on Computational Science, ICCS 2023

作者： Dubel, Rafal Wijata, Agata M. Nalepa, Jakub Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland

ISBN: (纸本)9783031360206

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Accident detection and road monitoring in real time using deep learning and lane detection algorithms 21

Accident detection and road monitoring in real time using de...

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4th International Conference on Machine Learning and Machine Intelligence, MLMI 2021

作者： Ayman Mounir, Shehab Eldeen Hussein, Walid H. Karam, Omar Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Egypt Computer Science Faculty of Informatics and Computer Science The British University in Egypt Egypt Information Systems Faculty of Informatics and Computer Science The British University in Egypt Egypt

ISBN: (纸本)9781450384247

Automating the monitoring of the roads would mean safer roads for both car drivers and pedestrians. The objectives of the system were to build a real time surveillance system for intelligent roads of the future. The system should be able to detect lane lines, cars, pedestrians and other moving vehicles on the road. In case a car goes off its course, the system should report it as an accident. Relying on machine learning technology, a real time detection based neural network was constructed to be trained on picking up objects in any given scene like cars, buses, pedestrians, bikes and motor bikes. Also, an algorithm was designed to detect lane lines and highlight them on the screen using image processing techniques and edge detection. The system achieved its main required target which is being able to detect objects in almost real time. © 2021 ACM.

关键词： Accidents

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Electromagnetic Sensor Behavior Prediction Using LSTM Neural Network for Enhanced Accuracy and Reliable Performance

Electromagnetic Sensor Behavior Prediction Using LSTM Neural...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Ahmad Sasani Mehrdad Ghafari Mostafa Eftekharizadeh Faculty of Electrical and Computer Engineering Semnan University Semnan Iran Chief Technical Officer Javaneh Inc. Tehran Iran Software Engineering Javaneh Inc. Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

Accurate sensors are essential in industries for system monitoring and control. High-precision sensor data can enhance predictions and prevent costly errors, especially in noisy environments that require detailed analysis. In this paper, we simulate the Long Short-Term Memory (LSTM) neural network model to predict the real magnetic signal from noisy data. The LSTM algorithm effectively removes Gaussian and random noise from the received data, providing accurate predictions of the real magnetic signals. Compared to previous works that used simpler methods, such as classical filters or basic models, our proposed algorithm demonstrates significantly higher accuracy. By using LSTM to remove noise and improve predictions, errors have been minimized, resulting in enhanced prediction accuracy. The simulation results indicate that the model can simulate the real behavior of the system with 97.67% accuracy, with minimal difference between the predicted and actual signals. This level of accuracy allows our model to significantly outperform previous methods in predicting magnetic signals in noisy environments and proves highly effective for industrial applications requiring precise accuracy.

关键词： Accuracy Magnetic sensors Noise Neural networks Predictive models Prediction algorithms Data models Sensor systems Noise measurement Long short term memory

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