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The Internet of Things (IoT) and Social Interaction: Influence of Source Attribution and Human Specialization
Full text linkThe evolvement of IT has open new doors in connecting many devices to the worldwide web that successively produce data around the physical setting using the IoT. However, the system of message turns out to be slightly intricate in human specialization-internet of things communication for the reason that the IoT is a system including diverse objects transferring data This study examines the hypothetical pathway by which the changes in source attribution that is multiple against single and specialization that is multi-functionality against single functionality of IoT devices affect the quality of human- internet of things interaction. The result from the study obtained from 80 participants that took part in the experiment shows that multiple source attribution improves the condition of information basically for the low-involvement people supports further probes the multiple source effects. However, this study recommends improvement of attribution source and human specialization-IoT
Computer Vision: A Timely Opportunity
Full text linkDifferent approaches to the study of computer vision have been taken into consideration. It begins with the collection of raw data and advances to methodologies and ideas that combine digital images, pattern recognition, machine learning, and computer graphics to produce new products, as well as new products themselves. In order to get crucial information, students can analyze images and videos to interpret events or descriptions, as well as discern patterns in the landscape, using computer vision. It makes use of a multi-spectrum application domain strategy in conjunction with a large amount of data in order to achieve its goals. In recent years, technological developments in computer vision have paved the way for the creation of novel agricultural applications. Precision yield forecasts for fruit and vegetable crops are particularly critical for improving harvesting, marketing, and logistics planning and execution. When a bridge is under stress or has a high volume of traffic, the geographical and temporal information provided by cars on the bridge reflects this. It is proposed to design a methodology for information gathering and dissemination by utilizing computer vision technology, which recognizes various items tracking and picture calibration via a quick regional neural convolution network, and a quick regional neural convolution network (Faster R-CNN). When dealing with small fish populations, it can be difficult to objectively assess the differences in behavior between individuals. The behavior of fish in aquaculture tanks has been studied with the use of a computer vision system that has been built in order to quantify these types of observations. Contained traffic load data is essential for bridge statistical analysis, security evaluation, and maintenance planning. This is particularly true for heavy trucks. From retail to agriculture, and across all industries, computer vision is having a big impact on organizations of all sizes and in all sectors. When a human eye is required to assess the situation, the significance of this becomes even more apparent. This paper provides information about computer vision technology, including short algorithms, issues, opportunities, and applications for computer vision in a range of fields in the year 2021, as well as information on computer vision in general. Information about computer vision applications in many fields is also included for the year 2021
A Survey of the Parameters of the Friction Stir Welding Process of Aluminum Alloys 6xxx Series
Full text linkFriction stir welding is a modern innovation in the welding processes technology, there are several ways in which this technology has to be investigated in order to refine and make it economically responsible. Aluminum alloys have strong mechanical properties when they are welded by using the Friction Stir welding. Therefore, certain parameters of the welding process need to be examined to achieve the required mechanical properties. In this project, a literature survey has been performed about the friction stir welding process and its parameters for 6xxx series aluminum alloys.
 
Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network
Full text linkThe recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods
Photo-Realistic 3D Models and Animations for Video Games and Films
Full text linkRealistic 3D objects are challenging to create. A detailed creation requires care if we can make remarkable detail and realism using techniques and a solid reference photo. In this post, we may learn how to construct beautiful, realistic scene objects using comprehensive procedures. This article shows how to use the Pen Tool and Blending Options to create a 3D animated scene. This work teaches simple Layer Styles to create beautiful shadows, lights, textures, and a realistic 3D feel. This work also shows how attractive illumination and rendering options may produce a natural scene. This article also shows how to make photo-realistic 3D models from digital images. This work teaches Illustrator pathways, texturing, essential lighting and rendering, texture application, and building and object parenting. This approach might even reconstruct tiny features or 3D items from 2D images. After a critical art stage, we can present the architecture of an animation design technique in this article. The goal is a conceptual and methodological lesson to approach this topic scientifically and practically. This goal also includes developing a scientifically sound paradigm to help comprehend realistic animation processes
The Difficulty of Learning Long-Term Dependencies with Gradient Flow in Recurrent Nets
Full text linkIn theory, recurrent networks (RN) can leverage their feedback connections to store activations as representations of recent input events. The most extensively used methods for learning what to put in short-term memory, on the other hand, take far too long to be practicable or do not work at all, especially when the time lags between inputs and instructor signals are long. They do not provide significant practical advantages over, the backdrop in feedforward networks with limited time windows, despite being theoretically fascinating. The goal of this article is to have a succinct overview of this rapidly evolving topic, with a focus on recent advancements. Also, we examine the asymptotic behavior of error gradients as a function of time lags to provide a hypothetical treatment of this topic. The methodology adopted in the study was to review some scholarly research papers on the subject matter to address the difficulty of learning long-term dependencies with gradient flow in recurrent nets. RNNs are the most general and powerful sequence learning algorithm currently available. Unlike Hidden Markov Models (HMMs), which have proven to be the most successful technique in a variety of sequence processing applications, they are not limited to discrete internal states and can represent continuous, dispersed sequences. As a result, they can address problems that no other method can. Conventional RNNs, on the other hand, are difficult to train due to the problem of vanishing gradients
Application of Convolution Neural Network for Digital Image Processing
Full text linkIn order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second step of becoming a standard global graphical model, communication networks are educated to deliver good local intelligence with a pixel. Convolutional Neural Networks (CNN or ConvNet) are complicated neural server networks in the field of artificial intelligence. Because of their great accuracy, convolutional neural networks (CNNs) are frequently utilized in picture categorization and recognition. In the late 1990s, Yann LeCun, a computer scientist, was based on the human notion of cognition and came up with the idea. When constructing a network, CNN uses a hierarchical model that eventually results in a convolution layer in which all neurons are linked and output is processed. Using an example of an image processing application, this article demonstrates how the CNN architecture is implemented in its entirety. You can utilize this to better comprehend the advantages of this current photography website.
 
PSO and ANN Based Hybrid MPPT Algorithm for Photovoltaic Array under Partial Shading Condition
Full text linkMPPT is an electronics device that extracts maximum available power from a PV module under varying environmental conditions. But most of the conventional MPPT methods fail to track maximum power under partial shading condition (PSC). Partial shading is the most common situation in PV power generation, which is caused if part of the series-connected strings is partially shaded. This situation leads to the multiple peaks in the P-V characteristics curve of the PV system. So stochastic search method, Particle Swarm Optimization (PSO), is used instead of the conventional methods to track maximum power under PSC. But the PSO method has the limitation of slow operation. So in this paper, a fast hybrid method is presented, which combines the PSO method with the ANN method. In this hybrid method, the ANN enables the existing PSO method to track MPP quickly by providing more accurate initial particle positions of the PSO algorithm
Internet of Things (IoT) in Agriculture: The Idea of Making the Fields Talk
Full text linkThe demand for agricultural crops is moving at a slower pace compared to the human population. There must be increased agricultural productivity. Ongoing innovative advances have added to the ascent of exactness agribusiness, empowering farmers to settle on better choices with more data about their soil, water, yield, and local environment, yet has been generally restricted to popularized and production of cash crops. The objective of smart agribusiness research is to ground a dynamic emotionally supportive network for the management of farms. IoT is being used in agriculture to get to know the crop field by utilizing sensors for monitoring, controlling in the field. It is used to get to know the crop field by utilizing sensors for monitoring, controlling in the field, etc. Recent developments in IoT, comparison between traditional and smart agriculture, and the roles of IoT in agriculture were analyzed in this study. The articles were purposively inspected while the qualitative data gathered was dissected utilizing content analysis. Summarily, the rise of smart agriculture has lowered the practice of traditional farming, as it has enhanced it in no small way. The research likewise showed that the lacuna in agriculture can be filled with IoT. Scalability in technology should be encouraged without affecting the functionalities of the existing infrastructures
A Green Integrated Inventory Model for a Three-Tier Supply Chain of an Agricultural Product
Full text linkGreen supply chain management coordinates environment issues into the supply chain business. It has been popular to both academicians and practitioners. Smooth supply of processed agricultural products is essential for human beings and pets. In some models, excess raw materials, byproducts and defected products are kept neglected in producing and marketing finished products. Here, we have presented a three-tier green supply chain model for an agricultural product where byproducts are used for some purposes. Solution procedure of the model is derived. We have demonstrated the model using two numerical example problems