A REVIEW ON MACHINE LEARNING STYLES IN COMPUTER VISION—TECHNIQUES AND FUTURE DIRECTIONS

ABSTARCT :

Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human.The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification.

EXISTING SYSTEM :

? Types of bogus are online websites or small companies, particularly start-up to create a mimic dataset for model training, creating the perfect dataset for research publication, offline survey collection, or nonexistent data. ? Identification of actual data from different sources and utilizes these collected datasets to find the unique outcome is one of the challenging tasks nowadays.

DISADVANTAGE :

? The problem arises when conduct questionnaires, people can’t respond 100% as we expected results received according to the filling of questionnaires by people and training and testing the model through vague data generates error ratio ? Compressed and large-scale images are the problems in the dataset to accurately detect objects, this will help to easily detect vehicle and monitor their actives ? The research of computer vision is still facing the problem of the location of human joints in images

PROPOSED SYSTEM :

? The journey of computer vision (CV) started in 1960, where father of computer vision Larry Roberts proposed 3D geometrical information extraction from 2D perspective polyhedral concepts in his Ph.D. thesis at MIT ? At the initial stage, CV illustrates, extraction of crucial information through digital images using computational models. Elaborating dual goals, vision is used as an autonomous system for the engineering point of views just like a human can perform a visual task, while computational models applied in the human biological system for detecting symptoms of diseases in the body

ADVANTAGE :

? Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification. ? SL, function to labelled training data, used to prediction. ? At last, SSL is the collaboration of both SL and USL, it has a small amount of labelled and large amount of unlabelled data, mainly used in information retrieval, image processing, and bio-information

An-Evaluation-of-the-Usefulness-of-Weather-Based (PDF)-Weather-to-garment-Weather-oriented-clothing Clothing-Recommendation-Based-on-Weather Fashion-Recommendation-Systems Dressing-recommendation-system-based-on-weather

FERTILIZER--Recommendation ML-models-for-a-dataset- Crop-based-pH temperature,-rainfall,-and-humidity CROP-AND-FERTILIZER-

Local-Food-Marketplace- E-COMMERCE-WEBSITE- FARMER-PRODUCT Online-farmer-Sales FARMER-PRODUCT

ANN- Healthcare-System- Depression classification-accuracy- FSVM-

Fingerprint-authentication- Fingerprint-based-attendance- Face-Recognization machine-learning biometric-recognition

WHEEL-CHAIR EMG-Monitoring Median-Filter Smart-Wheel-chair-control

Regression-Neural-Network-(GRNN) Probabilistic-Neural-Network-(PNN) Ada-Booster Breast-cancer- convolutional-neural-network-(CNN)-

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MACHINE-LEARNING- UNDERSTANDING-DEPTH-

MACHINE-LEARNING- UNDERSTANDING-DEPTH-

MACHINE-LEARNING- 3-D-DECONVOLUTIONAL-NETWORKS-

MACHINE-LEARNING- INTER-TURN-SHORT-CIRCUIT-

MACHINE-LEARNING- BASED-ON-JOURNALING-

MACHINE-LEARNING- FUSING-SELL-SIDE-ANALYST-

MACHINE-LEARNING- FAIRNESS-IN-SEMI-SUPERVISED-LEARNING

MACHINE-LEARNING- ENRICHING-THE-TRANSFER-LEARNING-

MACHINE-LEARNING- AUTOMATED-SCREENING-SYSTEM-

MACHINE-LEARNING- AN-ONLINE-TRANSFER-LEARNING-

MACHINE-LEARNING- THE-HIDDEN-SEXUAL-MINORITIES

MACHINE-LEARNING- DEPTH-ESTIMATION-