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Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Automated Facial Expression Recognition and Age Estimation Using Deep Learning

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computers, Materials & Continua 2022年第6期71卷 5235-5252页

作者： Syeda Amna Rizwan Yazeed Yasin Ghadi Ahmad Jalal Kibum Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi122612UAE Department of Human-Computer Interaction Hanyang UniversityAnsan15588Korea

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor *** proposed system first takes an input image pre-process it and then detects faces in the entire *** that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age *** proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups *** system achieved remarkable results over these benchmark datasets about recognition accuracy and computational *** proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

关键词： Feature extraction face expression model local transform features and recurrent neural network(RNN)

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An Explainable Predictive Model for Diabetes Detection Using Shapley Additive Explanations Approach

An Explainable Predictive Model for Diabetes Detection Using...

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Khan, Andleeb Yadav, Saundarya Nand, Parma Khanday, Akib Mohi Ud Din Bhushan, Bharat Jamil, Akhtar Hameedkhan, Alaa Ali Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan Department of Computer Engineering Faculty of Engineeringand Natural Sciences Istinye University Istanbul34396 Turkey

ISBN: (纸本)9783031709234

The chronic and incurable disease of diabetes that is not only marked with increasing prevalence but is also linked to other life-threatening diseases like cardiovascular diseases, tissue injuries and retinopathy. With this the early detection and diagnosis of the disease becomes essential. This paper, proposes a novel explainable predictive model for diabetes detection using Shapley additive explanations approach which will not just predict the disease but also highlight the most contributing features contributing to our result. Through the utilization of PIDD dataset, diabetes has been predicted by using three machine learning classifiers i.e. Random Forest, XGBoost and Support Vector Machine. The proposed explainable model outperforms the existing studies by giving an accuracy of 82%. The results were then interpreted using Shapley Additive Explanations (SHAP) which further explained that the most contributing features throughout the study in each model were ‘Glucose’, ‘BMI’ and ‘Age’ among others. This explanation is meant to aid the medical community in their diagnosis and effective recommendations to the patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Secure and Fast Remote Application-Based Authentication Dragonfly Using an LED Algorithm in Smart Buildings 6

Secure and Fast Remote Application-Based Authentication Drag...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Radhi, Batool Mohammed Hussain, Mohammed Abdulridha Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Education for Pure Sciences University of Basrah Department of Computer Science Basrah61004 Iraq Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bondo40601 Kenya

ISBN: (纸本)9798350344349

The proliferation of the internet of things (IoT) has led to the emergence of a wide range of intelligent devices, creating a broad domain with significant security concerns. These concerns impose a high level of security;unfortunately, IoT devices usually have limited resources in terms of little memory, low computing power, and a short battery life. Therefore, IoT application developers must use lightweight cryptographic tools to achieve a trade-off between performance and security. The storage and high computation capacity of cloud computing is often exploited to manage the vast amount of data produced by such gadgets. Some methods still suffer from attacks, and others cannot achieve low complexity. We propose a secure and low-complexity system for smart buildings in transferring data between the local server, the cloud, and users authorized by the owner. The LED encryption algorithm, which is lightweight and requires limited resources and less energy, was used to create a mobile application system characterized by confidentiality, authentication, and privacy. For further security, the owner's biometrics were used and derived as the key to decrypt data from the cloud. We have leveraged Dragonfly authentication technology to transfer data from the local server to the users. The owner can add authorized persons in the cloud database and local server to enjoy using the application. Moreover, we successfully balance security complexity and performance in our work. As a result, we achieve good results with a computation cost of 0.281 s and a communication cost of 1472 bit. © 2024 IEEE.

关键词： Authentication

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Sherif, Meriam Barakat, Nahla Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350367560

The paper proposes 'AdaptVR' a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique. © 2024 IEEE.

关键词： Virtual environments

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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Beyond Data Collection: Safeguarding User Privacy in Social Robotics 25

Beyond Data Collection: Safeguarding User Privacy in Social ...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Dorafshanian, Mahboobeh Aitsam, Muhammad Mejri, Mohamed Di Nuovo, Alessandro Laval University Department Of Computer Science & Software Engineering QC Canada Sheffield Hallam University Department Of Computing Sheffield United Kingdom

ISBN: (纸本)9798350340266

In an era marked by the advanced capabilities of social robots in personal and public spaces, the issue of pervasive data collection by these entities becomes increasingly pertinent. Social robots, deployed by government entities, hospitals, and corporations, are at the forefront of gathering sensitive personal data, necessitating careful consideration of privacy concerns. The vast amounts of data collected by these robots, while beneficial for decision-making and fostering research, also pose significant privacy risks. In particular, the challenge intensifies when robots collect and potentially share data that includes sensitive personal information. This paper presents a user-friendly Differential Privacy (DP) library that addresses this challenge. The library incorporates a risk threshold and evaluates the potential impact of data disclosure to accurately quantify privacy levels. Designed for nontechnical users, it enables the secure release of statistical data without the risk of privacy breaches. With privacy breaches and re-identification becoming increasingly common, this library offers a robust solution for safeguarding individuals' privacy while facilitating the sharing of valuable insights. © 2024 IEEE.

关键词： Sensitive data

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Customer Sentiments Towards Delivery Services in Bangladesh: A Machine Learning-Based Sentiment Analysis

Customer Sentiments Towards Delivery Services in Bangladesh:...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Bitto, Abu Kowshir Arman, Md. Shohel Saha, Rakhi Moni Jahan, Hasnur Mahmud, Imran Das, Aka Daffodil International University Department of Software Engineering Dhaka1207 Bangladesh Premier University Department of Computer Science Chattogram4203 Bangladesh

ISBN: (纸本)9798350350289

Bangladesh has a large population, which is causing the delivery system to grow up, day by day. Therefore, several companies that provide these delivery services usually referred to as 'Currier Service', are growing gradually. In this paper, we focus on the top delivery service companies in Bangladesh. We analyze customer sentiment based on reviews and comments collected from their social media pages. We collected data of customers' comments from the verified social media pages of Sundorban Currier Services, Redx Currier Services, and Pathao Delivery System. We used a combination of Natural Language Processing and machine learning models to categorize Bangla sentiment reviews based on N-gram features. We used the Unigram, Bigram, and Trigram methods to identify the number of negative and positive reviews on real-life social media platforms and to predict whether new reviews were negative or positive. We have found that the Bigram feature is the best for this analysis because it has the highest accuracy of 90.72%. Using the Bangla NLP approach, the machine learning models are categorized in negative and positive reviews with the sentiment analysis method. © 2024 IEEE.

关键词： Sentiment analysis

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