IoT integrated fuzzy classification analysis for detecting adulterants in cow milk

Lal, Prashant P.; Prakash, Avishay A.; Chand, Anand; Prasad, Kushal A.; Mehta, Utkal; Assaf, Mansour H.; Mani, Francis S.; Mamun, K. A.

doi:10.1016/j.sbsr.2022.100486

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…To measure the pH of the solution, the SEN0161-V2 Analog pH meter is used, which can be visualised in figure 3.7. sent to the Arduino, where the pH value of the medium is then interpreted. Generally speaking, a higher voltage obtained is reflected in a lower pH value (more acidic medium) [53].…”

Section: Analog Ph Sensor -Sen0161-v2mentioning

confidence: 99%

Application of Artificial Intelligence Techniques to a Sensorial Module

Pimentel,

Gonçalves,

Pinto

et al. 2024

2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE)

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Nowadays, society is in a climate of uncertainty regarding energy and resource sustainability, which is why efforts to combat this problem are made and encouraged daily. This is no different in the industry, where there is a continuous work to improve manufacturing processes in order to achieve utopian zero defect manufacturing. Alongside this, the emergence of new technologies, the development of the computing power of machines and the digitisation of processes in the context of Industry 4.0 have been fundamental to the production of data that feeds and develops artificial intelligence models.This dissertation will address a quality control problem in a welding process in the automotive industry. It is believed that the development of a predictive quality control system using artificial intelligence techniques can solve the problem in question.In order to study the problem, and due to the impossibility of working with the real problem, this work includes a study of the techniques expected to be applied in the final solution. As well as being theoretical, this study of methods has a practical component, applying them to data collected from a testbed. This testbed is a physical prototype with a parameterised process, also developed as part of this dissertation. In addition, a dataset referring to a laser welding process was also analysed to complement the study of the algorithms with data similar to data from the original problem.In this document, the scope of the work is framed within the current state of the art, followed by a theoretical reflection on the subjects covered. It is decided to study and apply the following supervised learning techniques to the data collected: K-Nearest Neighbours, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression, Random Forest and Gradient Boosting. This is followed by a description of the test base developed and the methodology for collecting and processing the data.It is concluded that although the models built with the Support Vector Machine algorithms showed satisfactory results for modelling the laser welding process, the data collected from the test base was unreliable, and different models can perform differently in different situations.

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Section: Analog Ph Sensor -Sen0161-v2mentioning

confidence: 99%

Application of Artificial Intelligence Techniques to a Sensorial Module

Pimentel,

Gonçalves,

Pinto

et al. 2024

2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE)

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“…Eckelamp e Bowley (2020) realizaram um estudo com diferentes produtores que fizeram a aquisição de máquinas de precisão para gado de leite, onde estas conseguem fazer a identificação de animais doentes, como com mastite, por meio de "machine learning", por meio da sua mudança de comportamento. Lal et al (2022) indicaram o uso de novas tecnologias baseadas em inteligência artificial, onde estas tem tido rápido desenvolvimento e perspectivas de que no futuro será muito prático a utilização de bases de dados para atuar na pecuária leiteira com maior precisão. Os autores apresentam a utilização das tecnologias baseadas em lógica fuzzy, que é um método de inteligência artificial, visam a classificação de leites de acordo com seus atributos e detecção de adulterantes no leite.…”

Section: Aplicações Da Ia Na Pecuáriaunclassified

Uso de Inteligência Artificial na Pecuária: Revisão de literatura

Silva

Santos

Machado

et al. 2023

RSD

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O uso de soluções tecnológicas concede melhorias na eficiência e sustentabilidade de sistema em agropecuária, como o uso da inteligência artificial (IA), que é um campo interdisciplinar alterando o paradigma agrícola para algo diferente do atual. As soluções baseadas em IA não apenas permitem que os produtores façam mais com menos, mas também melhoram a qualidade e garantem que as safras cheguem ao mercado mais rapidamente. Diante da relevância do tema e das informações apresentadas, o presente trabalho de revisão integrativa de literatura tem como objetivo abordar e evidenciar a importância e possibilidade de uso da Inteligência Artificial na Pecuária. Foi realizado uma revisão bibliográfica para o levantamento de informações e de dados disponíveis sobre o uso e aplicação da inteligência artificial na pecuária, onde a busca e compilado dos dados ocorreu através das ferramentas de pesquisa do Google, Google Acadêmico, SCIELO –Scientific Eletronic Library e Ministério da Agricultura, Pecuária e Abastecimento. Ao observarmos a utilização da inteligência artificial na pecuária podemos inferir que a implementação desta proporciona a possibilidade de identificação e contagem de animais de forma remota, comportamento animal e formação de um banco de dados, de forma mais precisa, de um animal individual ou de uma propriedade, permitindo ao produtor e técnico responsável traçar estratégias que maximizem a produção e reduza os custos, além de desenvolver um negócio sustentável.

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“…On the other hand, fuzzy inference systems are widely used in nonlinear and not very predictive models, even being recently used in research related to COVID-19 [20], bacterial analysis [21], and food adulteration [22], as well as their integration with neuro convolutional systems [23]. In turn, there are several applications of fuzzy models for alarm generation systems [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Automatic food bio-hazard detection system

Moreno

Baquero

2023

IJECE

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This paper presents the design of a convolutional neural network architecture oriented to the detection of food waste, to generate a low, medium, or critical-level alarm. An architecture based on four convolution layers is used, for which a database of 100 samples is prepared. The database is used with the different hyperparameters that make up the final architecture, after the training process. By means of confusion matrix analysis, a 100% performance of the network is obtained, which delivers its output to a fuzzy system that, depending on the duration of the detection time, generates the different alarm levels associated with the risk.

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IoT integrated fuzzy classification analysis for detecting adulterants in cow milk

Cited by 8 publications

References 36 publications

Application of Artificial Intelligence Techniques to a Sensorial Module

Application of Artificial Intelligence Techniques to a Sensorial Module

Uso de Inteligência Artificial na Pecuária: Revisão de literatura

Automatic food bio-hazard detection system

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