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16th International Conference on Information technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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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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A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase

A Mutation-Guided Assessment of Acceleration Approaches for ...

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International Mining software Repositories, MSR

作者： Zhili Zeng Tao Xiao Maxime Lamothe Hideaki Hata Shane McIntosh Software REBELs University of Waterloo Canada Division of Information Science Nara Institute of Science and Technology Japan Department of Computer Engineering and Software Engineering Polytechnique Montréal Canada Faculty of Engineering Shinshu University Japan

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Continuous Integration (CI) is a popular software development practice that quickly verifies updates to codebases. To cope with the ever-increasing demand for faster software releases, CI acceleration approaches have been proposed; however, adoption of CI acceleration is not without risks. For example, CI acceleration products may mislabel change sets (e.g., a build labeled as failing that passes in an unaccelerated setting or vice versa) or produce results that are inconsistent with an unaccelerated build (e.g., the underlying reasons for failure differ between (un)accelerated builds). These inconsistencies threaten the trustworthiness of CI acceleration *** this paper, we propose an approach inspired by mutation testing to systematically evaluate the trustworthiness of CI acceleration. We apply our approach to YourBase, a program analysis-based CI acceleration product, and uncover issues that hinder its trustworthiness. First, we study how often the same build in accelerated and unaccelerated CI settings produce different mutation testing outcomes. We call mutants with different outcomes in the two settings "gap mutants". Next, we study the code locations where gap mutants appear. Finally, we inspect gap mutants to understand why acceleration causes them to survive. Our analysis of ten open-source projects uncovers 2,237 gap mutants. We find that: (1) the gap mutants account for 0.11%–23.50% of the studied mutants; (2) 88.95% of gap mutants can be mapped to specific source code functions and classes using the dependency representation of the studied CI acceleration product; and (3) 69% of gap mutants survive CI acceleration due to deterministic reasons that can be classified into six fault patterns. Our results show that even deterministic CI acceleration solutions suffer from trustworthiness limitations, and highlight the ways in which trustworthiness could be pragmatically improved.

关键词： Accelerometers Codes Source coding Life estimation software Complexity theory Reliability

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

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computers, Materials & Continua 2022年第1期70卷 963-979页

作者： Awais Mahmood Muhammad Imran Aun Irtaza Qammar Abbas Habib Dhahri Esam Mohammed Asem Othman Arif Jamal Malik Aaqif Afzaal Abbasi College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(Islamabad Campus) 44000Pakistan University of Engineering and Technology Taxila Pakistan International Islamic University Islamabad44000Pakistan Faculty of Sciences and Technology University of KairouanSidi Bouzid4352Tunisia Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Searching images fromthe large image databases is one of the potential research areas of multimedia *** most challenging task for nay CBIR system is to capture the high level semantic of *** researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback(RF).However existing RF based approaches needs a number of iteration to fulfill user’s *** paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the *** previous research work it is reported that SVM based RF approach generating better results for ***,this paper focused on SVM based RF *** enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)before applying SVM on user *** main objective of using thesemeta-heuristic was to increase the positive image sample size from *** steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO,finally SVM is applied using the positive sample generated through *** proposed technique is named as Particle Swarm Optimization Genetic Algorithm-Support Vector Machine Relevance Feedback(PSO-G A-SVMRF).Precisions,recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 *** experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

关键词： Feature selection image retrieval particle swarm optimization

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Research Square

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Research Square 2021年

作者： Chen, Bo-Lun Zhu, Guo-Chang Sheng, Yi-Yun Xie, Qian Ji, Min Yu, Yong-Tao Faculty of Computer and software Engineering Huaiyin Institute of Technology Jiangsu Huaian223003 China

Air quality is related to people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. As an important algorithm for signal analysis, empirical mode decomposition can analyze the change trend of air quality well, smooth the complex and changeable air quality data, and get the change trend of air quality under different time scales. According to the change trend under different time scales, the extreme learning machine is used for training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that the signal decomposition fuzzy prediction model has a good learning ability and has good accuracy in predicting the concentration of various pollutants in air quality. © 2021, CC BY.

关键词： Health

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Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

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National Science Review 2024年第9期11卷 181-191页

作者： Shimaa Holail Tamer Saleh Xiongwu Xiao Jing Xiao Gui-Song Xia Zhenfeng Shao Mi Wang Jianya Gong Deren Li State Key Laboratoryof Information Engineering in Surveying Mapping and Remote SensingWuhan University Geomatics Engineering Department Faculty of Engineering at Shoubra Benha University School of Computer Science Wuhan University Collaborative Innovation Center of Geospatial Technology National Engineering Research Center for Multi-media Software Institute of Artificial IntelligenceWuhan University

War-related urban destruction is a significant global concern, impacting national security, social stability,people's survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed Luo Jia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel–Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total—15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged—which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The Luo Jia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global *** first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.

关键词： war damage monitoring satellite remote sensing missile crater detection type of damaged building building damage level detection Gaza Strip

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