The authors have delved into residual learning framework to ease the training of deep neural networks that are substantially deeper than those used previously. Besides, the research paper explicitly reformulates the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. The research also delves into how comprehensive empirical evidence show that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
This article was authored by David G. These can be utilized to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The paper additionally delves into an approach which leverages these features for image recognition.
This approach can help identify objects among clutter and occlusion while achieving near real-time performance. Deep neural nets with a large number of parameters are very powerful machine learning systems.
However, overfitting is a serious problem in such networks. The central premise of the paper is to drop units along with their connections from the neural network during training, thus preventing units from co-adapting too much.
This helps in significantly reducing overfitting, while furnishing major improvements over other regularization methods. Induction of decision trees: Quinlan , this scientific paper was originally published in and summarizes an approach to synthesizing decision trees that has been used in a variety of systems.
The paper specifically describes one such system, ID3, in detail. Additionally, the paper discusses a reported shortcoming of the basic algorithm , besides comparing the two methods of overcoming it. To conclude the paper, the author presents illustrations of current research directions. Convolutional Neural Networks CNNs are proven to stand as a powerful class of models for image recognition problems. These results encouraged the authors to provide an extensive empirical evaluation of CNNs on large-scale video classification.
This was accomplished using a new dataset of 1 million YouTube videos belonging to classes. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference: The paper was published in Judea Pearl is the author to this article. The paper presents a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty.
Pearl furnishes a provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism , truth maintenance systems, and nonmonotonic logic. With a background in Engineering, Amit has assumed the mantle of content analyst at Analytics India Magazine. An audiophile most of the times, with a soul consumed by wanderlust, he strives ahead in the disruptive technology space.
When authors co-submit and publish a method article in MethodsX, it appears on ScienceDirect linked to the original research article in this journal.
Submit Your Paper Enter your login details below. Username Password I forgot my password Register new account. Track Your Paper Check submitted paper Due to migration of article submission systems, please check the status of your submitted manuscript in the relevant system below: Username Password I forgot my password. Track accepted paper Once production of your article has started, you can track the status of your article via Track Your Accepted Article.
CiteScore values are based on citation counts in a given year e. More about CiteScore Impact Factor: View More on Journal Insights.
Publishing your article with us has many benefits, such as having access to a personal dashboard: This free service is available to anyone who has published and whose publication is in Scopus. Researcher Academy Author Services Try out personalized alert features.
Most Downloaded Artificial Intelligence Articles. The most downloaded articles from Artificial Intelligence in the last 90 days. Artificial cognition for social human—robot interaction: An implementation June Creativity and artificial intelligence August Selection of relevant features and examples in machine learning December Argumentation in artificial intelligence July—October
Research Paper on Artificial Intelligence August 24, writer Research Papers 0 Artificial Intelligence or AI is an artificially created intelligence and the name of the branch of mainly computer science that seeks to understand and develop the AI theory and functioning, and tries to build intelligent systems.
The most downloaded articles from Artificial Intelligence in the last 90 days. Menu. Search. Search. Search in: All. Webpages. Books. it appears on ScienceDirect linked to the original research article in this journal Human-level artificial general intelligence and the possibility of a technological singularity A reaction to Ray.
JAIR is published by AI Access Foundation, a nonprofit public charity whose purpose is to facilitate the dissemination of scientific results in artificial intelligence. JAIR, established in , was one of the first open-access scientific journals on the Web, and has been a leading publication venue since its inception. Artificial Intelligence This Research Paper Artificial Intelligence and other 64,+ term papers, college essay examples and free essays are available now on beginstartx0.gq Autor: review • December 18, • Research Paper • 2, Words (11 Pages) • 1, Views.4/4(1).
Artificial intelligence research paper. CHAPTER 1. INTRODUCTION. Introduction. The use of ontology and artificial intelligence in the management and control of cybercrime is in a major increase since it is crucial in handling voluminous data which are very crucial for organizational performance. - This research Paper has problems with formatting ABSTRACT Current neural network technology is the most progressive of the artificial intelligence systems today. Applications of neural networks have made the transition from laboratory curiosities to large, successful commercial applications.