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QoS Prediction Model of Cloud Services Based on Deep Learning

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

Mudahnya BM: A Context-Aware Mobile Cloud Learning Applicati...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Muhammad, Sufri Admodisastro, Novia Osman, Hafeez Mohd Ali, Norhayati Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Seri Kembangan Selangor43400 Malaysia

ISBN: (纸本)9783030902346

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Ontology Driven Testing Strategies for IoT Applications

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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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Ontological View-driven Intensional Semantic Integration for information Systems in a Decentralized Environment 14

Ontological View-driven Intensional Semantic Integration for...

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14th International Conference on Knowledge engineering and Ontology Development, KEOD 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Ali Adhnouss, Fateh Mohamed Ali El-Asfour, Husam M. McIsaac, Kenneth El-Feghia, Idris Aburukba, Raafat Wahaishi, Abdulmutalib Dept. of Electrical & Computer Engineering University of Western London Canada Faculty of Information Technology Misurata University Libya Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Software Engineering Rochester Institute of Technology New York United States

ISBN: (纸本)9789897586149

Ontologies are an essential component of semantic integration approaches for information systems . In a decentralized environment, each specification of the domain reflects an Ontological view. However, the semantics characterization of information systems in such decentralized environment poses a significant issue related to their integration. information systems are viewed as independent intensional entities with their own beliefs, distinct from those held by others. Such autonomy is distorted by traditional extensional semantics. Other entities’ beliefs are introduced into a given entity, thus affecting their beliefs. Additionally, the information that one entity provides to another entity may not be consistent with the information known by the latter. We need an alternative semantics for information integration, which is not dependent on the extension, but rather on the underlying conceptualization. This paper proposes a classification of the environment where the information systems lives and a novel modelling paradigm for information integration using intensional logic to model the ontological views. The model comprises a formal modeling approach for the conceptualization as well as for the semantic integration process. Copyright © 2022 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved.

关键词： Semantics

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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

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Computer Systems Science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis

Facial Recognition Utilizing the CLM Model and Supervised Ma...

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information technology (ACIT)

作者： Musab Iqtait Jafar Ababneh Amer Abu-Jassar Mohammad Al-Refai Mohammad Dmour Mohammad Arabiat Ruaa Binsaddig Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Cyber Security Department Faculty of Information Technology Zarqa University Zarqa Jordan College of of Computer Sciences and Information Amman Arab University University Amman Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Finance College of Business Administration University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE.

关键词： Machine learning algorithms Social networking (online) Face recognition Supervised learning Lighting Machine learning Nearest neighbor methods Feature extraction software information technology

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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Deep Learning Hierarchical Methods for Insect Pest Recognition on Plants 23

Deep Learning Hierarchical Methods for Insect Pest Recogniti...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Diep, Tuong-Nghiem Tran, Thien-Phuc Ho-Ngoc, Vinh-Phat Yang, Tuan-Anh Do, Trong-Le Faculty of Information Technology University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam Software Engineering Lab University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798400708916

Insect pests have always been a global agricultural problem because the severity and extent of their occurrence threaten crop yield. Recognizing them early can help farmers have efficient measures to handle them, which can help mitigate negative impacts from insect pests. However, insect pest recognition still relies heavily on experts, which is expensive and time-consuming. With the power of Deep Learning, we propose two methods to solve this task in this paper. First, we proposed a method that uses models pre-trained on the ImageNet dataset, including ResNet-50, EfficientNet-B4, and VisionTransformer-B16, respectively. We also change the structure of these models by adding a Dropout layer before the output layer of these pre-trained models to avoid overfitting. Second, we apply hierarchical learning for this task. In the latter approach, we first use the baseline model to create a confusion matrix. Through this matrix, we cluster classes that the baseline model misses to each other because of the similar appearance across classes into bigger classes, and we consider them as sub-datasets. Then, we build each model for each sub-dataset using the identical backbones as the baseline methods with the hope that it helps the method classify better in these classes. We do experiments to evaluate the performance of methods on the IP102 dataset. From experiments, our proposed method, which uses VisionTransformer-B16 backbone combined with hierarchical learning, gets the best accuracy of 74.50% on the IP102 dataset. When ensemble 3 above models and combine with hierarchical learning, we get the best accuracy of 76.24% on this dataset. © 2023 ACM.

关键词： Agriculture

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Exploiting the sequential nature of genomic data for improved analysis and identification

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Computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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New Textural Features for Handwritten Signature Image Verification 7

New Textural Features for Handwritten Signature Image Verifi...

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7th International Conference on engineering, Applied Sciences and technology, ICEAST 2021

作者： Soisang, Suriya Poomrittigul, Suvit Pathumwan Institute of Technology Faculty of Engineertng L Electrical Engineering Department Bangkok Thailand Pathumwan Institute of Technology Faculty of Science and Technology 2 Software Engineering and Information System Department Bangkok Thailand

ISBN: (纸本)9781665441223

In this paper, a new textural feature for solving offline handwritten signature verification is proposed. A new textural features method is developed by combining a Local Binary Patterns (LBP) method and a Gradient Quantization Angle (GQA) method. This proposed method is called Local Binary Patterns with Gradient Quantization Angle (LBPGQA), as developed by heuristic method to improve the precision of verification the offline signature image. The hypothesis for this study is to classify the distinctive handwritten signature individually with the actual signature angle and refraction for enhancing the signature fraud detection. The verification step is achieved by Artificial Neural Network (ANN) classifier and trained on genuine signatures. Furthermore, the test stage is performed on genuine signatures and skilled forgeries. The experiments are conducted on CEDAR datasets. The experimental results show that in the LBPGQA method outperforms classical features such as Histogram of oriented gradients and local binary patterns. Conclusively, this proposed method can verify the individual and distinctive handwritten signature and help to protect the signature fraud by skilled forgeries. © 2021 IEEE.

关键词： Local binary pattern

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