Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

基于膨胀实验数据获取角膜屈光手术后力学参数的方法初探

目的:基于离体膨胀实验获得的角膜顶点与压力数据,探索利用有限元方法获得屈光手术后角膜力学参数的方法。方法:取实验兔4只,左眼为手术眼,右眼为对照眼。行标准的LASIK术实验兔2只,仅制瓣未对角膜基质层消融的实验兔2只,分别于术后不同时间点实施离体角膜膨胀实验。在离体膨胀实验中,利用位移传感器、压力传感器和显微镜分别获得角膜顶点位移、角膜内压力及角膜正侧面轮廓图像。根据获得的角膜轮廓图像构建角膜几何模型,用二阶Ogden模型描述角膜的本构关系,通过有限元方法模拟膨胀实验,将计算结果与膨胀实验数据比对确定角膜的力学参数,分析屈光手术后饲养一定时间时角膜的力学特性。结果:角膜膨胀实验获得的角膜顶点位移与压力呈非线性关系。在相同压力下,术后饲养一定时间后的兔眼角膜顶点位移量比对照眼小。二阶Ogden模型可以较好地描述屈光手术后角膜的力学特性。屈光手术后角膜弹性模量较对照眼大。结论:基于整体膨胀实验数据,利用有限元方法模拟角膜膨胀实验反推屈光手术后角膜力学参数的方法是可行的。

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

Personality Detection Using Context Based Emotions in Cognitive Agents

Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction.