Machine Learning and Artificial Intelligence in Banking

Donepudi, Praveen Kumar

doi:10.18034/ei.v5i2.490

Cited by 49 publications

(27 citation statements)

References 7 publications

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“…Mainly in this type of operation we convolute Robotic servo motor actions with our sensing camera input. Actions are totally dependent on our receiving data sets from sensors (Donepudi, 2017). Through these repeated exercises, we developed a self-learning environment for our robots and these data set analyses can be used on any outside world regardless of the robotic shape and camera resolution properties.…”

Section: Methodsmentioning

confidence: 99%

Reinforcement Learning for Robotic Grasping and Manipulation: A Review

Donepudi¹

2020

Asia Pac. j. energy environ.

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A century of robots is the 21st century. The robots have long been able to cross the divide between the virtual universe and the real world. Robotics, as the most successful contender in the upcoming great technological revolution, will play an ever more important role in society because of the impact it has in every field of life, including medicine, healthcare, architecture, manufacturing and food supplies, logistics and transport. This document introduces a modern approach to the grasp of robots, which draws grasp techniques from the human demonstration and combines these strategies into a grasp-planning framework, in order to produce a viable insight into the objective geometry and manipulation tasks of the object. Our study findings show that grasping strategies of the form of grasp and thumbs positioning are not only necessary for human grasp but also significant restrictions on posture and wrist posture which greatly reduce both the robot hand's workplace and the search space for grasp planning. In the simulation and with a true robotic system this method has been extensively tested for several everyday living representative objects. In the experiment with varying degrees of perceiving in certainties, we have demonstrated the power of our method.

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Section: Methodsmentioning

confidence: 99%

Reinforcement Learning for Robotic Grasping and Manipulation: A Review

Donepudi¹

2020

Asia Pac. j. energy environ.

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“…technologies. Several surveys on the facilities in the health sectors and interviews with the health workers also served to obtain the data that concerned advances in the technologies of health facilities and their diagnosis processes of different diseases (Donepudi, 2017b). Surveys in the industrial field of innovative application of A.I.…”

Section: Methodsmentioning

confidence: 99%

The Impact of Artificial Intelligence on Innovation

Fadziso¹

2018

Glob. discl. econ. bus.

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Artificial intelligence has the potential to greatly increase the efficiency of the current life in which we live. However, it may result in more impacts through its application as the modern mode in invention and bring a new perspective of the existing innovation processes in the organization of R & D. Application of machines' intelligence such as robots from the recent development is a vivid example of invention brought about by the innovation filtered via artificial intelligence. Innovative ways through invention have a sense of replacement in man's duties in the world's varied economic sectors. Large datasets and algorithms will be used in research industries, and the latter will result in potential racing, monitored incentives by large companies, and particular algorithms. However, transparency and transfer of information between public and private will be the engine source to stimulate healthy inventions and innovation programs shortly. Rapid advancement in the artificial intelligence arena has significant sound impacts on society as far as the economy is concerned. Production and characteristics of many products and services have a high potentiality to be directly influenced by these innovations, and important productivity, competition, and employment implications. Even though these innovations will positively influence the largest proportion of human lives, artificial intelligence (A.I.) can potentially change its innovation processes, accompanied by approximately thoughtful consequences, and may gradually dominate the direct consequence.

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“…A social media information can be named as untrustworthy, one-sided, and hard to decipher. Social media data is temperamental because isn't anything but difficult to survey the reliability of its root or distributer; social media is hard to decipher because the users use local language to post their content and the content may contain spelling and linguistic blunders; lastly, it tends to be expressed that social media information can be one-sided since, on account of road accidents, not all the relevant data about accidents are reported by the road users (Donepudi, 2017). The untrustworthiness of social media can be located by utilizing strategies that remove the time, captivity, and subject of the report to connect the report to a genuine event; to manage the arrangement with the syntactic complexities of social media, a technique was proposed dependent on a deep learning engineering and a strategy was depicted dependent on a convolutional recurrent network; the two papers expect to overpass the restriction of the sack of-words and predefined set of keywords techniques that are generally utilized to handle the content of the tweets.…”

Section: Social Mediamentioning

confidence: 99%

Road Accident Analysis and Prediction using Machine Learning Algorithmic Approaches

Ballamudi¹

2019

Asian j. humanity, art lit.

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Ongoing studies have anticipated that in 2030, car crashes will be the fifth driving reason for death around the world. The main cause of car crashes is difficult to decide these days because of a complex mix of qualities like the mental condition of the driver, road conditions, climate conditions, traffic, and infringement of traffic rules to give some examples. The expenses of fatalities and driver wounds because of car crashes incredibly influence the general public. The use of machine learning methods in the field of road accidents is picking up speed nowadays. The organization of machine learning classifiers has swapped conventional data mining methods for creating higher outcomes and exactness. This work presents a review of different existing businesses related to accident prediction utilizing the machine learning area. Wounds because of road accidents are one of the most pervasive reasons for death separated from health-related issues. The investigation of road accident seriousness was finished by running an accident dataset through a few machine learning arrangement calculations to see which model played out the best in characterizing the accidents into severity classes, for example, slight, extreme, and fatal. It was seen that calculated relapse to perform multilevel order gave the most noteworthy exactness score. It was additionally seen that variables, for example, the number of vehicles, lighting conditions, and road highlights assumed a part in deciding the seriousness of the accident. Engineers and analysts in the car business have attempted to plan and manufacture more secure vehicles, yet auto collisions are unavoidable. Examples associated with hazardous accidents could be identified by building up a prediction model that naturally orders the sort of injury severity of different traffic accidents. These social and roadway designs are valuable in the improvement of traffic security control strategies. Significantly, estimates be founded on logical and target reviews of the reasons for accidents and the seriousness of injuries. This paper presents a few models to predict the seriousness of the injury that happened during traffic accidents utilizing machine-learning paradigms. We considered networks prepared to utilize machine learning methods. Analysis results uncover that among the machine learning ideal models considered different standards paradigm approaches.

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Machine Learning and Artificial Intelligence in Banking

Cited by 49 publications

References 7 publications

Reinforcement Learning for Robotic Grasping and Manipulation: A Review

Reinforcement Learning for Robotic Grasping and Manipulation: A Review

The Impact of Artificial Intelligence on Innovation

Road Accident Analysis and Prediction using Machine Learning Algorithmic Approaches

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