G Venkatesan (70 results)

Condition: New. pp. 187.

Condition: New. pp. 187 1st Published.

Condition: New. pp. 187.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

N.A. Condition: New. ISBN:9788184874273,190pp N.A.

N.A. Condition: New. ISBN:9788184873498,248pp N.A.

Paperback. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Condition: New.

Hardcover. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Condition: As New. Unread book in perfect condition.

Condition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in good condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:9781842658819.…

Hardcover. Condition: New. 1st Edition. Contents: 1. Current status of insecticide resistance in arthropods. 2. Insect physiology with reference to action of insecticides. 3. Neurotoxide insecticides and their mode of action. 4. Insecticides resistance: a micro evolutionary phenomena. 5. Entomopathogens: mode of action and mechanism of resistance. 6. Overview of pesticide resistance mechanisms. 7. Reduced cuticular penetration and metabolic resistance. 8. Target site resistance. 9. Conventional methods of detection of insecticide resistance. 10. Biochemical and molecular methods of detections of insecticides. 11. Fitness cost associated with insecticides resistance. 12. Functional significance of insect endosymbionts. 13. Insect endosymbionts: metagenomic perspective. 14. Do insects show resistant to EPN? Role of insect immunity. 15. Insecticide resistance management in insect pests. 16. Bt crops and insects pests susceptibility: current status. 17. Pesticide induced hormesis and resurgence. 18. Practical aspects of resistance detection: bioassay methods. 19. Practical aspects of resistance detection: biochemical methods. 20. Clean laboratory practices for rearing and bioassay of insects. …

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

Condition: New.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

Condition: New.

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: New.

Hardcover. Condition: new. Hardcover. BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New.

Condition: New. In English.

Hardcover. Condition: new. Hardcover. BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condition: New.