Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

Singh, Gurwinder; Sethi, Ganesh Kumar; Singh, Sartajvir

doi:10.1007/s42979-021-00929-6

Cited by 16 publications

(5 citation statements)

References 98 publications

(144 reference statements)

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“…Object Detection [3][4][5][6] plays a key role in this project. It's a computer vision task that involves identifying and localizing objects within an image/video [7,8].…”

Section: B Object Detectionmentioning

confidence: 99%

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YOLOInvoiceWizard: Revolutionizing Invoice Generation through Object Detection

Teja,

Desu,

Vamsikrishna

et al. 2023

Preprint

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This research paper introduces a novel approach to streamline the customer checkout process in retail stores through the integration of computer vision technology using YOLOv8 for object detection. The project's focus lies in facilitating efficient invoice generation by detecting products, identifying them accurately, and seamlessly adding them to a web-based graphical user interface (GUI) shopping cart. The proposed system aims to change the conventional billing procedure by leveraging computer vision capabilities to expedite the checkout process, minimizing customer wait times, and reducing human errors during billing. By harnessing the power of YOLOv8, a state-of-the-art object detection model, the system can identify various products placed on the counter. The detected items are added into a user-friendly web GUI cart that allows customers to conveniently track their selections while navigating the store. The integration of computer vision and web technology creates a unique and delightful customer experience during the checkout process, contributing to increased customer satisfaction and loyalty. The potential benefits of this project are multifaceted, encompassing reduced wait times at the billing counter, enhanced accuracy in billing processes, and overall improved efficiency in retail operations. The research paper concludes with a detailed evaluation of the prototype system's performance, validating its effectiveness in enhancing customer experiences and optimizing billing procedures in a real-world retail environment. This paper contributes to the advancement of computer vision applications in the retail sector, setting the stage for future innovations in customer-centric shopping experiences.

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“…Object Detection [3][4][5][6] plays a key role in this project. It's a computer vision task that involves identifying and localizing objects within an image/video [7,8].…”

Section: B Object Detectionmentioning

confidence: 99%

“…3) Data Annotation: The process of labelling or tagging data to provide ground truth information for training models is known as data annotation [3,4,15]. Dataset of images needs to be annotated individually with respect to the class the image belongs to.…”

Section: B Object Detectionmentioning

confidence: 99%

YOLOInvoiceWizard: Revolutionizing Invoice Generation through Object Detection

Teja,

Desu,

Vamsikrishna

et al. 2023

Preprint

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“…Employing machine learning (ML) algorithms enables an enhancement in task prediction without explicit hand-coding (Singh et al 2007). ML approaches aim at developing prediction techniques automatically by extracting statistically robust structures in the analyzed data.…”

Section: Introductionmentioning

confidence: 99%

Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

Salmanpour¹,

Bakhtiary²,

Hosseinzadeh³

et al. 2023

Phys. Med. Biol.

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Objectives: Parkinson's disease (PD) is a complex neurodegenerative disorder, affecting 2–3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc., bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods: We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson's Progression-Markers-Initiative database. We generated 4 datasets, namely using (i,ii) year-0 (D1) or year-1 (D2) features, iii) longitudinal data (D3, putting datasets in years 0&1 longitudinally next to each other), and iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0&1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction (AEA) and 10 feature selection algorithms (FSA) followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results: When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved a MAE of 1.74±0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4+Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05±0.25 with a hold-out testing performance of 0.57. Conclusion: Our study shows the importance of using larger datasets (timeless), and utilizing optimized HMLSs, for significantly improved prediction of MoCA in PD patients.

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“…Concerning (ii) and (iii), information can be extracted from sensors by applying machine learning (ML) methods [31,32]. These learning methods can be supervised or unsupervised.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

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As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.

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Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

Cited by 16 publications

References 98 publications

YOLOInvoiceWizard: Revolutionizing Invoice Generation through Object Detection

YOLOInvoiceWizard: Revolutionizing Invoice Generation through Object Detection

Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

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