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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Smart Detection of Passive Smokers using Machine Learning 8

Smart Detection of Passive Smokers using Machine Learning

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Shah, Pratham Parekh, Astha Bajaj, Divyansh Swain, Debabrata Kumar, Manish Bhilare, Amol Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Pandit Deendayal Energy University Department of Electronics and Communication Engineering Gandhinagar India Vishwakarma Institute of Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This has become the source of a great concern, especially amongst the young generation. Machine Learning is used to find a pattern of biological attributes to identify possible smokers. The process of teaching a machine to do intelligent classification from a sizable archive of historical data is known as machine learning. The aim of this paper is to develop a learning model that can detect passive smokers through features like eyesight, weight, fasting blood sugar, cholesterol and more. On this dataset, a number of data preprocessing operations are carried out to build a reliable model which include checking for null values , encoding the data , balancing the dataset and scaling it and selection of highly correlated features. A variety of supervised learning models are employed in the model training process to create a robust machine learning model. From the set of models applied, K-Nearest Neighbour (KNN) (84 %) and Random Forest (RF) (86 %) gave the highest accuracy. However, implementing Random Forest with Hyperparameter tuning using GridSearchCV resulted in a higher accuracy (87 %). © 2024 IEEE.

关键词： Decision trees

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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Classification and Segmentation of Leaf Images based on Deep Learning for Peanut Plant Disease Detection 4

Classification and Segmentation of Leaf Images based on Deep...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ebenezer, V. Daniel, Arsha Anna Getzi Jeba Lillipushpam, P. Edwin, E.Bijolin Roshni Thanka, M. Devassy, Rosebel Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. The seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. The variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Brain Tumor Classification using MR Images and Transfer Learning 2

Brain Tumor Classification using MR Images and Transfer Lear...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Gupta, Manu Shirisha, Gangadi Chandana, Aela Mani Ankitha, Belide Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350347579

A brain tumor is the irregular development of a mass tissue within the brain or close to it, which has the capacity to spread and duplicate the wildly affecting organs within the body. Brain tumor classification is a vital assignment to assess the tumors and make a treatment choice concurring with their classes. In this study, a model for the classification of brain tumors from MRI images utilizing transfer learning is developed. The convolution neural network (CNN), VGG16 and InceptionV3 techniques are applied to extract features and perform classification with brain MRI image dataset as input. The precision, accuracy, recall, and f1-score metrics are computed to evaluate the performance of the developed model. The results obtained show that the proposed pre-trained model VGG16 achieved better results than other models with an accuracy of 98%. © 2023 IEEE.

关键词： Magnetic resonance

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SentiMatch: Sentiment Analysis of Multi-modal Social Media Posts 10

SentiMatch: Sentiment Analysis of Multi-modal Social Media P...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Yadav, Abhishek Jain, Nisarg Sharma, Lakshay Singh, Preety The Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665490993

Social media users are increasingly using images and videos to communicate their feelings. However, researchers usually rely on textual data for sentiment analysis. In this paper, we focus on Twitter posts having both text and image data and analyze the match between their sentiments, when predicted independently. We also propose a method to predict the sentiment of a social media post as a collective entity from the individual sentiments of text and image. Comparing our proposed model to a naive approach where the sentiment of the text is assigned to the image also, we see an improvement of around 20% in prediction of sentiment of Twitter images associated with text. © 2023 IEEE.

关键词： Sentiment analysis

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A Comprehensive analysis of 5G Communication and Its Health Care Management Models 2

A Comprehensive analysis of 5G Communication and Its Health ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar Sagar, Lalit Goel, Dipti Gupta, Sonali Saraswat, Manish Sharma, Ashish Srm Institute of Science and Technology Delhi Ncr Campus Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Department of Information Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Gla University Department of Computer Engineering & Applications UP Mathura India

ISBN: (纸本)9798350371055

This paper provides an in-intensity examination of 5G conversation and its health care management models. 5G era, the imminent 5th technology of cellular cell communications, is expected to revolutionize the cellular enterprise with its build-up of excessive velocity, low latency, and big capability. however, it also increases new demanding situations for healthcaredelivery, in addition to data protection, privateness and tool management. This paper will are seeking for to deal with those troubles by using exploring numerous 5G fitness care management fashions. extra mainly, this paper will discover the demanding situations of 5G in healthcare, how the current healthcare infrastructure can facilitate 5G's use, and the principle elements of numerous 5G healthcare management fashions. finally, this paper will discuss the destiny implications of 5G in healthcare, and what groups want to do in order to make certain the most green and cozy healthcare shipping. by means of delving into this rely, this paper will provide a complete view of 5G conversation and its healthcare control fashions, main to progressed healthcare delivery within the close to future. This paper presents a complete analysis of fifth-technology (5G) communique applied to healthcare control fashions. To make sure functionality and efficacy., 5G networks and their related services need to adhere to a strict set of requirements and requirements, which can be evaluated in regard to latency, community useful resource requirements, and safety protocols. several important additives and capabilities of 5G-enabled healthcare models are mentioned, which include far off patient monitoring offerings, personalized healthcare services, and pervasive healthcare get right of entry to. additionally, this paper discusses the measures for successful implementation of 5G technology into healthcare, in conjunction with some potential dangers and ethical issues that have to be taken under consideration. in the end, the pap

关键词： cellular demanding models revolutionize

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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High-Frequency Performance with Microwave and Millimeter-Wave Methods 1

High-Frequency Performance with Microwave and Millimeter-Wav...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Poornima, D. Jany Shabu, S.L. Refonaa, J. Karunya, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Modern wireless communication, radar systems, and the realization of new technologies like 5G and beyond all rely heavily on high-frequency performance. The use of microwave and millimeter-wave techniques (MMWT) could provide revolutionary new uses for vast amounts of data at extremely low latency. High-frequency performance optimization is still a challenging and essential problem. Interference Mitigation, Antenna Design, Signal Processing, and Regulatory Compliance are all considered to be sizeable obstacles towards accomplishing High-Frequency Performance. The motive of the research is to advocate the use of Regulatory Compliance Frequency Band Analysis (RCFBA) for the purpose of figuring out target frequencies in accordance with utility necessities and acting spectrum scanning and evaluation with the intention to assess regulatory constraints and available channels. Maximizing signal energy even as keeping off interference via cautiously choosing and positioning antennas. Enabling higher data throughput, decrease latency, and a wider variety of applications for high-frequency systems by way of easing their adoption and deployment in fields like telecommunications, radar systems, satellite communique, and 5G networks. High-frequency performance can be modelled and evaluated with the help of present day modeling tools, that may then be used to optimize gadget parameters, propagation, and antenna designs. It equips users with the way for thorough simulation evaluation, optimization, and fulfillment in excessive-frequency packages, taking into account the deployment of excessive-frequency structures that healthy the desires of contemporary packages and regulatory standards. © 2023 IEEE.

关键词： Regulatory compliance

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Credit Risk Prediction using Genetic Algorithm and Stacking Ensemble

Credit Risk Prediction using Genetic Algorithm and Stacking ...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Barthe, Shashank Biswas, Saroj Kumar Sanyal, Saptarsi Purkayastha, Biswajit National Institute of Technology Department of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798350338003

It is not an easy task to estimate the likelihood that a borrower will fulfill his or her duty to the lender (bank). There are several factors to take into account before deciding whether to extend any kind of loan. This decision-making process calls for sufficient quantitative data to compute, which takes time and is susceptible to error by humans. To finish this difficult and busy work, An Machine learning model for Credit Risk Prediction using Stacking Ensemble (ESCRPGSE) has been proposed as a solution to this time-consuming and difficult task. It combines oversampling technique, 'Gaussian Smote' to solve the imbalance problem, Genetic Algorithm to select useful features that are useful for model training, and a Stacking ensemble bagging technique for classification. By implementing 10-fold cross-validation, the proposed model's performance is evaluated in terms of accuracy and f1-score, and it is then contrasted with standard single-classifier classifier models, ensemble-based models, and various models that have been published in the literature. © 2023 IEEE.

关键词： Genetic algorithms

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