Friction stir welding (FSW) is a relatively modern process which is considered a suitable permanent join method for different industrial applications such as aerospace and automotive. Moreover, friction stir welding is preferred in contrast with other welding methods due to its ability to join similar and dissimilar metals with high efficiency. The current study presents a review for new researches taking in the consideration FSW process variables like welding speed, tool rotational speed and pin geometry for different metals alloys in addition to failure modes occurred in these alloys.

Optical character recognition (OCR) of Devanagari script characters has been a popular research topic for many years and still a lot of work is yet to be done. The strive to achieve utmost accuracy in recognizing the characters is ever-increasing and a task yet to be accomplished. The complexity of this particular script having very peculiar features has attracted many researchers to contribute in this field and showcase their work. Building an OCR for any script involves various phases, the two of vital importance being segmentation and recognition. In this paper, we will review each of the two in detail, by observing the existing methodologies used and their probable scope of improvement.

Drowsiness is one of the main contributing factor in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. A new approach towards automobile safety and security in an autonomous area is primarily expected on the automotive system. To overcome this problem, here is a solution namely the driver drowsiness alert system, which gives an alert by watching each driver’s eye movements in real – time environment. In a process of detecting a person falling asleep, a real time eye detection of the person is acquired. The image processing of an eye through mood detection and adjacent to count of heart rate is analyzed over time to identify drowsiness and fatigue situations, and characteristics indicative of the person falling dormant are determined. To determine openings and closings of the eyes, eye aspect ratio is analyzed to determine the width and height of the eyes, mood discernment analysis through Keras algorithm and heart rate diagnosis manoeuvring heart beat sensor.

The ultimate aim of agriculture or farming in India is not only limited to growing of crops but is also associated with the economic growth of farmers and labours. Rice is one of the staple food crop of our country. Basically in India establishment of rice depends on the availability of moisture, climatic condition, age of the variety, availability of inputs & human labour. Mechanization in agricultural sector is advancing in developing countries like India. Rice is a labour-intensive crop and requires about 80-90 labour days per acre. Timely availability of labour and water for various activities of rice is becoming a problem. Hence to overcome these issues there is a need of mechanization in the field of rice cultivation by using rice transplanter as major tool in this process. There is also need for designing and developing an economical and user-friendly rice transplanter for small scale farmers in order to increase the production as well as the quality of rice. In this paper manual rice planting machine along with their merits and demerits has been discussed by studying various aspects of transplantation related to rice and its field performance which are beneficial to the society and farmers. A rice transplanter is specialized equipment best fitted to transplant rice seedlings on the wet muddy paddy field. This paper is focused on developing a machine which addresses labour problems faced by small scale farmers. The newly developed rice planting machine, can harvest upto two rows of paddy at a time. Outcomes are Reduction in manpower by 80%., Reduction in time by 50%, Reduction in production cost by 50%, Increase in product quality.

For the past few years, neural networks are maturing, and the application domain for the neural network is increasing. Due to the rise in unstructured Image-based data, theres a need for understanding data based upon visual features, not on textual data. Image Captioning in Deep learning is the process to understand different objects in the image, try to build a relation between those objects, and give a sentence that is semantically and syntactically correct. To serve this purpose, Image Captioning uses the combined architecture of Computer Vision and NLP-based Networks as Encoder-Decoder Architecture. In this Survey paper, we discuss the paper[1], which proposed to use Efficient CNN-LSTM based Network using Network Compression. The Network uses CNN-based VGG-16 as encoder and LSTM as decoder network. Network techniques are used such as Quantization and Pruning, to reduce model size up to 73.1% and reduce inference time up to 71.3% and increase BLEU score to 7.7% as compared to uncompressed network.

We propose in this paper a fully automated deep learning model, which learns to classify human actions. Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. Human activity recognition (HAR) is the outcome of a similar motive. Recent times have seen the theory of Human Activity Recognition (HAR) catering to multiple challenging applications built on the increase in ubiquitous, persuasive computing.

Heart rate is the number of heartbeats per minute which is the number of contractions of the ventricles (the lower chambers of the heart). For some human beings it might also be too excessive (tachycardia) or too low (bradycardia) which can cause clinical problems. In the current pandemic situation estimating or monitoring heart rate of a subject with the help of instruments can be risky as it involves physical contact and also one has to visit the clinic which might not be possible all the time. Research focused on non-contact based systems has increased over the past years. Existing systems include those which require contact, some having restrictions on skin tone, and others often involve high costs and complex application of sensors. This paper focuses on real time monitoring of Heart rate of multiple people simultaneously. It is obtained through a real time video using a webcam of laptop/computer. As blood circulation causes facial skin variation, therefore facial video is considered. Signal processing methodssuch as Fast Fourier Transform have been applied on the region extracted after applying colour magnification channels in video recordings. Application also sends a notification via a text message to the specified contact number if the Heart rate is below or above a given range. This algorithm is easy to implement, low cost and simple for real time application. Testing included trials to calculate heart rate of multiple people simultaneously. Once such a trial involved detection of faces of four people and their Heart rate calculation at the same time. Measured value of Heart rate in comparison with the ideal method was within justifiable range. Further, work can be done on environmental conditions which can be very useful in many real time applications.

This project is aimed at the design and fabrication of small size oil extraction machine from groundnuts. The objectives are aimed at providing a base for the commercial production of the machine, using locally available raw materials at a relatively low cost. There is so much wastage of these nuts on farms since a negligible portion is consumedby the harvesters. This work is intended to help solve some of the problems hindering a successful design and fabrication of oil extraction machine from groundnuts.

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

It takes lots of effort to change your health motivation into an actionable workout schedule. A companion to join in your goals stimulates your motivation as well as acts as a challenging opportunity to grow in the journey. We propose a social networking platform where different users are suggested using User Profile-based recommendation systems using personality analysis. This system helps users to connect with the community or individuals as a companion. With the help of the companion aspect, the daily workout schedule for users and their connections in a network is more simplified, where the possibility of getting demotivated or lost is reduced. A friend recommendation engine is hence needed to provide a good way to diminish this problem as well as satisfy user needs. A recommendation engine facilitates the users by helping them in making an informed decision based on the information they need, like item recommendations based on users’ previous behavior and the information on them collected by the system. Hence, proposing the use of a personality-based friend recommendation framework, which consists of a 3-Layered Artificial Neural Network for friend preference classification and a distance-based sorted subset selection procedure for friend recommendation.

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

The technology of pneumatics has gained tremendous importance in the field of workplace rationalization and automation from old-fashioned timber works and coal mines to modern machine shops and space robots. It is therefore important that technicians and engineers should have a good knowledge of pneumatic system, air operated valves and accessories. The aim is to design and develop a control system based an intelligent electronically controlled automotive bumper activation system called "Intelligent Braking with Pneumatic Bumper ". This system consists of IR transmitter and Receiver circuit, Control Unit, Pneumatic bumper system and braking unit. This output is given to logic circuit to indicate the final output i.e. alarm and the control signal is given to the bumper activation system braking unit. The pneumatic bumper system is used to provide safety to the man and vehicle.

As of now breast malignant growth discovery is a very significant job for overall ladies to save the life. Specialist with radio calculated having chance to miss the anomaly due to naiveté in field of malignancy location. The preprocessing is the main advance in the mammogram examination because of poor caught mammogram picture quality. Preprocessing is very critical for addressing and change the roentgenogram picture to the additional investigation with preparing. There are Different sorts of sifting strategies are accessible for preprocessing. This channels used to improve picture quality, eliminate the commotion, safeguards the edges inside a picture, improve and smoothen the picture. In this paper, we implemented different channels in particular, normal channel, versatile middle `channel, normal or mean channel, and wiener channel.

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.