Holograms for teaching are an advanced teaching tool that enhances the efficiency of student participation in the process of learning. The three-dimensional visualizations in holograms will give students a better opportunity to achieve knowledge interactively and entertainingly, which could explain some very complex concepts better and help provide better understanding. This would help students visualize what they cannot view directly with ordinary means: anatomy, historical items, scientific models, etc. Besides, holograms can enable distant learning through the realistic and life-like representation of subjects, beating the barriers of geographical distance. Though there are challenges, including cost and technical limitations, the potential of holographic learning tools in classrooms is huge; these make education more dynamic and engaging.
To reduce the total cost of power generation, power grid networks are becoming more integrated for the purpose of power transmission. With insufficient control, power systems become more difficult to operate and less secure. The economics of alternating current power transmission have always compelled planners to transmit as much power as possible through a given transmission line. Many networks have crowded smaller and thermally limited lines, while other higher capacity lines run well below their thermal maximum. When series capacitors are used in higher voltage cables, power can be transferred from overloaded lines, maximising the use of the existing line and supplementing the power system's performance. The aim of this paper is to demonstrate efficient line network utilisation for maximum power flow through the intended line with series capacitor compensation using a three-line meshed power system network with different thermal line limits. The simulations using the PowerWorld simulator show that adding a series capacitor increases the power transfer through the line before it reaches its thermal limit.
Purpose – Micro-influencers are often positioned between common social media users and celebrities. Using them for endorsements is a current and prospective aspect of social media marketing. This paper studies the credibility of microinfluencers and their impact on behavioral intentions of their followers. Designing – This paper take benefit of the qualitative analysis on responses received from 135 Instagram users who follow at least one micro-influencer and have purchased at least one product promoted by them. Responses from 15 microinfluencers and 08 small business owners (who have roped in regional micro-influencers) have also been studied. Findings – The paper identifies that micro-influencers are gaining much popularity in India, since the users find them relatable and trustworthy. The attractive advertisements (in form of posts & stories) leave an impact on followers and also shape their purchase and viral intentions. Research limitations/implicationsThe methodology adopted for this paper is of qualitative nature and the responses collected from various sources were verified against the available literature. Same could be done in qualitative terms. Practical implications The use of micro-influencers for social media marketing is on the rise. Studying their impact especially for small businesses that can’t spend on other cost intensive advertising alternatives becomes relevant. Originality/value -This research contributes to studying microinfluencer role in affecting the behavioral intentions of social media users in India. Not many studies have been conducted in this regard and even less considering Instagram as a platform
Gender is a central feature of our personality still. In our social life it is also an significant element. Artificial intelligence age predictions can be used in many fields, such as smart human-machine interface growth , health, cosmetics, electronic commerce etc. The prediction of people's sex and age from their facial images is an ongoing and active problem of research. The researchers suggested a number of methods to resolve this problem, but the criteria and actual performance are still inadequate. A statistical pattern recognition approach for solving this problem is proposed in this project.Convolutionary Neural Network (ConvNet / CNN), a Deep Learning algorithm, is used as an extractor of features in the proposed solution. CNN takes input images and assigns value to different aspects / objects (learnable weights and biases) of the image and can differentiate between them. ConvNet requires much less preprocessing than other classification algorithms. While the filters are hand-made in primitive methods, ConvNets can learn these filters / features with adequate training.In this research, face images of individuals have been trained with convolutionary neural networks, and age and sex with a high rate of success have been predicted. More than 20,000 images are containing age, gender and ethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, and resolution.
Today we are living in the digital world, with a systematic life, which may leads to many new diseases due to artificial production on agriculture, mental stress, economic and social stress too. Due to machine world, patients hearts diseases can be predict by various heart diseases detection model. There are various techniques, models and tools are predicted to find the real status of heart diseases which may have advantages and disadvantages too. This paper will try to improve the performance of the new proposed techniques which is used to determine the drawback from the existing system and overcome the drawback. The proposed techniques is used to preprocess the information and moved to the next process of selection to determine accuracy, sensitivity, specificity, precision, recall and F-measure from the dataset retrieved from three major metropolitan cities likes Chennai, Bangalore and Delhi. These proposed techniques provide the more efficient and effective with the existing system with 90% to 95%.
