Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

Surya, Velappa Jayaraman; Senthilselvi, A.

doi:10.1007/s00521-021-06829-3

Cited by 15 publications

(3 citation statements)

References 48 publications

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“…Deep learning network plays vital role in all area of research such as food adulteration [53], noise removal [54], mobile phone recycling process [55], and sign language understanding [56]. The optimization algorithm provides an optimized solution in all area of researches such as noise removal [57,58], micro-grid protection [59], food adulteration [60], etc.…”

Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

R K Chandana Mani,

J Kamalakannan,

Y. Pandu Rangaiah

et al. 2023

JAIT

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Breast cancer is one of the deadly cancer among the female population, and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Though deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional Neural Networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with Logistic Regression as a binary classifier. Since hyper-parameters of every CNN has a closer impact on the performance of the entire deep learning model, our method focus on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm, The proposed model performs classification of Breast Histopathology images into benign and malignant images and produce better results. We use Break His Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.

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Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

R K Chandana Mani,

J Kamalakannan,

Y. Pandu Rangaiah

et al. 2023

JAIT

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“…We 1st start by offering a track record on CPS and also dependability evaluation. Machine learning now plays a crucial part in all research fields, including the detection of food adulteration (Zhou et al, 2019;Surya & Senthilselvi, 2020), the diagnosis of sickness (Surya & Senthilselvi, 2022;Gnana Sophia et al, 2023), and the determination of metal purity (Sankareswaran & Krishnan, 2022;Senthilselvi et al, 2020), among others.…”

Section: Literature Reviewmentioning

confidence: 99%

Depend Ability Analysis on CPS Using Machine Learning Techniques

2023

JAETS

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A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the crucial analysis of nodes in CPS application program as well as help support the setting up of CPS software phone Secondly, a real-time CPS dependability automated internet analysis technique is suggested. Unreliable methods are able to imply big losses, each monetarily in addition to within man's life. On a good mention, CPS has information like the main component of the operation of theirs. The prevalence and availability of information demonstrate a brand-new chance to change the methods within what dependability evaluation continues to be usually performed. This process utilizes printer mastering tips to create an analysis framework, design, and style an internet queuing algorithm, as well as put into action real-time internet evaluation and analysis of CPS dependability. Preventive steps make sure that the device works ordinarily as well as with no interruption, which significantly betters method dependability. Last but not least, simulation final results verify the usefulness of the analysis technique and the broad application prospects of its.

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“…Liu et al, use Terahertz spectra to evaluate peanut oil's peroxide value (POV) and employ genetic algorithms (GA) and principal component analysis (PCA) to process THz data, ultimately achieving better correlation between absorbance spectra and POV [20]. Surya et al, implement Long Short-Term Memory (LSTM) with the seagull optimization algorithm (SOA) for improved classification and authenticity assessment, outperforming existing techniques [21]. Dou et al utilize headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) to detect sesame oil adulteration effectively, distinguishing authentic and counterfeit products using chemometric methods [22].…”

Section: Introductionmentioning

confidence: 99%

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Ku,

Lung,

Chi

2023

Foods

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Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

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Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

Cited by 15 publications

References 48 publications

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

Depend Ability Analysis on CPS Using Machine Learning Techniques

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

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