Improved Particle Swarm Optimization for Detection of Pancreatic Tumor using Split and Merge Algorithm

Dhruv, Bhawna; Mittal, Neetu; Modi, Megha

doi:10.1080/21681163.2021.1966650

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…In actual work, cars will encounter many common complex situations and phenomena, which will lead to various interference factors affecting normal operation. Engine fault diagnosis is to analyze and judge these parameters according to the basic information of the car, such as various sensor elements, ignition timing signals and fuel systems, and to detect these parameters and analyze their characteristic curves as well as their causes and change trends, Then convert their numerical values into corresponding digital waveforms (or images) displayed in the oscilloscope through calculation methods, and then use MATLAB software to develop an algorithm program to realize the engine fault diagnosis process [7][8]. This mainly includes checking whether all systems of the vehicle are normal, fuel consumption, ignition timing, etc.…”

Section: Working Principle Of Engine Fault Diagnosismentioning

confidence: 99%

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

2023

KME

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The automobile engine is a high-speed vehicle. Its working principle and process are to generate gas through fuel burning, and then convert the chemical energy contained in the exhaust gas into heat energy. However, in reality, due to various reasons, serious wear and tear inside the engine and sharp reduction of mechanical strength have occurred. This paper mainly introduces a method based on particle swarm optimization algorithm to simplify the complex structure between various systems in the car and outside. The analysis shows that this method has the advantages of accuracy and reliability, and can effectively diagnose and predict it. The advantage of large workload and small space is very prominent. After that, this paper designs a fault diagnosis model of automobile engine based on particle swarm optimization algorithm, and tests the model. The test results show that the fault diagnosis time of this model is short, and the fault diagnosis accuracy is high, which can meet the user needs.

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Section: Working Principle Of Engine Fault Diagnosismentioning

confidence: 99%

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

2023

KME

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“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

“…Confusion matrix [18,20,35,40,45] 5 (17) Reported [16,17,19,21,[23][24][25][26][27][28][29][30][31][32][33][34][36][37][38]41,42,44]…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

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“…As a beneficial supplement to centralized power generation, distributed power generation can enhance power supply stability. Distributed power generation has unparalleled advantages in energy substitution, environmental protection, improving system power supply reliability and reducing power supply costs [1][2].…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

2022

DPS

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Distributed power generation has unparalleled advantages in energy substitution, environmental protection, improving system power supply reliability and reducing power supply costs. The purpose of this paper is to study the application of the improved particle swarm optimization algorithm(IPSO) in the location selection and sizing of distributed power generation. This paper first briefly introduces the characteristics of today's main distributed energy resources, and empirically analyzes the impact of distributed energy access on the distribution network. On this basis, a single-objective optimization model considering several constraints is established for the minimum investment cost and net loss of distributed generation. By examining the shortcomings of the standard particle swarm optimization algorithm, combining the optimization algorithm with various optimization techniques, a hybrid particle swarm optimization algorithm is proposed, and the effectiveness of the algorithm is studied. The optimization model is determined through the algorithm improvement, which further confirms the feasibility and effectiveness of the model and algorithm improvement. Then, in view of the environmental problems most concerned by modern society, advanced multi-objective algorithms are used to realize the overall optimization of power station location and power for distributed generation, and to provide decision makers with a variety of solutions. Experiments have shown that the overall optimization scheme in this paper can save about 20% of the cost, and through optimization, the network loss voltage can be reduced by about 30%.

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Improved Particle Swarm Optimization for Detection of Pancreatic Tumor using Split and Merge Algorithm

Cited by 9 publications

References 29 publications

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Improved Particle Swarm Optimization Algorithm in Site Selection and Capacity of Distributed Power Supply

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