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Ontology Driven Testing Strategies for IoT Applications

Computers, Materials & Continua 2022年第3期70卷 5855-5869页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Muhammad Usman Ashraf Shafiq Ahmad Ahmed T.Soliman Shahzada Khurram Muhammad Shafiq Jin-Ghoo Choi INP-ENIT University of ToulouseTarbes65000France Department of Software Engineering Superior UniversityLahore54000Pakistan University of Management and Technology Sialkot CampusPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Internet-of-Things(IoT)has attained a major share in embedded software *** new era of specialized intelligent systems requires adaptation of customized software engineering ***,software engineering has merged the development phases with the technologies provided by industrial *** improvements are still required in testing phase for the software developed to IoT *** research aims to assist in developing the testing strategies for IoT applications,therein ontology has been adopted as a knowledge representation technique to different software engineering *** proposed ontological model renders 101 methodology by using Protégé.After completion,the ontology was evaluated in three-dimensional view by the domain experts of software testing,IoT and ontology *** results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for *** Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

关键词： Ontology software testing internet of things automation

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Optimizing Edge computing Usability with Random Forest Algorithms

Optimizing Edge Computing Usability with Random Forest Algor...

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computing (ICOCO), IEEE International Conference on

作者： Esraq Humayun Kok Cheng Lim Evelyn Yeap Ali Selamat Nguyet Quang Do Mohd Hazli Mohamed Zabil Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computing Universiti Tenega Nasional Kuala Lumpur Malaysia Department of Computing Universiti Tecnologi Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

This study convincingly demonstrates the immense potential of Random Forest algorithms to significantly enhance usability in edge computing environments by concentrating on critical parameters such as execution time, energy consumption, and scalability. By leveraging the power of the Random Forest technique, we train models using a multitude of decision trees, which not only improves prediction accuracy but also effectively combats overfitting. This approach directly addresses pressing challenges, including limited resources, scalability issues, and the unpredictability of network conditions. Our results are impressive: we achieved a noteworthy reduction in mean execution time by 0.73 seconds and a standard deviation of 1.15 seconds. These metrics highlight the model’s remarkable resilience and reliability in boosting system responsiveness. With these advancements, Random Forest stands out as a formidable solution for the demanding real-time processing needs inherent in edge computing. Our research presents a compelling strategy for establishing robust and effective edge computing infrastructures by closing crucial gaps that currently exist, particularly the lack of scalable solutions for large, heterogeneous edge platforms, adaptability in fluctuating contexts, and real-world validation. In summary, our work lays a solid foundation for further exploration of adaptive Random Forest applications that can seamlessly adjust to dynamic network conditions, reinforcing the significance of edge computing in latency-sensitive, nextgeneration applications.

关键词： Adaptation models Scalability Computational modeling Heuristic algorithms Real-time systems Usability Random forests Standards Optimization Edge computing

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VExSearch: Improving Visualizations Results for Web-based Information Exploration and Refinement

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第1期48卷 1-15页

作者： Mahdi, Mohammed Najah Ahmad, Abdul Rahim Ismail, Roslan Buhari, Adamu Muhammad Universiti Tenaga Nasional Malaysia Universiti Tenaga Nasional Malaysia Software Engineering Department College of College of Computing and Informatics Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Faculty of Engineering Multimedia University Malaysia

As the web grows, finding information from large data repositories has become increasingly difficult not only due to inadequate number of results that are relevant, but also due to poor sorting of relevant results from those irrelevant. The present search engines (SEs) use the query and response lookup process that does not provide precise results. Thus, researchers have gone beyond the paradigm to explore a new class of method to seek information, which is called exploratory research that is open-ended and its faceted search can improve the overall search process. Besides, many studies have begun tapping into enhancement of web search results relevancy. The web reflects vast heterogeneity, varying structure, and massive in volumes. Therefore, it is rather difficult to seek accurate outcomes as desired. As such, visualisation and interactive graphics have been proposed as methods to manage massive amounts of results and to project essential features for the web pages. Additionally, search engine controls reconstruction and reformulation of queries. As such, a search engine is presented in this study by developing it on the cloud computing platform environment The search engine is based on the idea of improving visual exploratory search (VExSearch) while exploring information in the web. This particular notion reflects the process of seeking and combing through the vast information by using the coordinated visualisation method, apart from minimising the effort spent in seeking information per query. The VExSearch was evaluated for its capability and performance and later compared with IMDb SE and CloudMining SE. The comparative results showed that the VExSearch was 66% more accurate than the other SEs. VExSearch also seemed to provide the most relevant results among all the three SEs, aside from attaining an average improvement of 20% in terms of recall. © 2021, IAENG International Journal of Computer Science. All rights reserved.

关键词： Search engines

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An Approach for Early Detection of Schizophrenia

An Approach for Early Detection of Schizophrenia

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International Conference on computing and Networking Technology (ICCNT)

作者： Sachin Paranagama Isiwara Uditha Pamodya Daundasekara Shehani Hettiarachchi Lokesha Weerasinghe Dharshana Kasthurirathna Department of Computer Science and Software Engineering Faculty Of Computing Sri Lanka Institue Of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively.

关键词： Computer science Emotion recognition Computer vision Accuracy Face recognition Psychology Machine learning Transformers Vectors Convolutional neural networks

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A Soft Voting Classification Model for Network Traffic Prediction in VANET/V2X

A Soft Voting Classification Model for Network Traffic Predi...

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

作者： Parvin Ahmadi Doval Amiri Samuel Pierre Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory Polytechnique Montreal Montreal Canada Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory (LARIM) Polytechnique Montreal Montreal Canada

Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability.

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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PraK Tool: An Interactive Search Tool Based on Video Data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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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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Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition 21

Texture and Orientation-based Feature Extraction for Robust ...

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21st IEEE/ACIS International Conference on software engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

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