International Research Journal on Advanced Science Hub

Emotion plays a vital role in medical research and interpersonal communication. Essentially feeling can be communicated verbally like discourse or non-verbally like outward appearance and physiological signals. A human emotion is complex physiological state which involves a physiological response, a person’s experience and behavioral change. EEG measures electric current that are generated due to neuronal activities in the human brain. This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals. Our analysis is based on extracted features and classification methods of emotion recognition. We intended that, this study will be useful for newly researchers those entering in the field of emotion recognition.

Face detection systems are growing exponentially. Newly emerged technologies are also being involved in the management applications. But they had failed to compensate at least anyone of the essential aspects of the system such as scalability, security, personalization, etc. This paper presents a fundamental platform that provides the ways and techniques to intelligently use the integration of Artificial Intelligence and Blockchain in which AI is used to detect and recognize the face and Blockchain maintains the tamper-proof records. This convergence will provide a tamper-proof and rapidly working A&D access management system for a trust management system that can be used for attendance in an organization & for many other purposes.

Communication has become the essential part of our lives over various technologies. As the large the number of users gets connected to internet everyday it becomes one of the problem to provide better communication services to every users. Text messaging has become common and many users are getting attracted towards video meetings. Application has problem in connecting large number of users in a video meeting for the purpose of communication. In this project report a new approach to make communication easier has been proposed. Users can quickly register themselves and then can access the applications which provide users the channels functionality. A user can either create their channel or else join other channels. Inside channels they can communicate using text messaging and also video conferencing with large number of users present in a channel. Users can share their screen and text each other while attending video meeting. Application can also generate attendance report of users in channels who attended video meeting

Education is a powerful weapon which prepares the individual to face the difficulties of life, come out of poverty fear, and help to achieve status in society. At present scenario, continuous learning and updating is very essential for everyone. As per the frequent changes in technology people also should learn continuously. Maximum members of present generation are stepping into the higher education and are growing fast due to the availability of plenty of learning resources outside the classroom. Education becomes more global by the help of internet and technological devices, applications used by the institutions, teachers and the students are really appreciable in making each and every one smart enough to lead the life in modern era. To reach the fullest potential we should encourage the use of Technology in present education. In this paper author made an attempt of analysing overall outcome of technology in education fields.

The central nervous system controls all the functions of the body. The brain is the vital organ of our body and it can be suffered from various diseases. In order to treat various brain diseases, the physicians use Magnetic Resonance Imaging (MRI) technique in recent days for the treatment. Manual analysis and classification of brain images into normal or deceased is a tedious task. So different supervised learning techniques are used in this purpose. In this paper, Independent Component Analysis (ICA) has been used for feature reduction and Multilayer Perceptron (MLP) has been used for classification task. The experimental study is conducted on two of the brain image datasets i.e. Glioma and Alzheimer and the results suggested that ICA-MLP produced better results than MLP.

In the real data world, there are various clustering algorithms available in data mining. The data available from the different data sources may be huge in instances, attributes and in different formats. The clustering algorithms available are assessed based on how the algorithm cluster the given data and find its parametric values.  The clustering of data may end in inappropriate results if the algorithm is not chosen wisely. This paper proposes a comparison between diverse clustering algorithms such as K Means clustering, Mini-Batch K Means clustering, Hierarchical clustering, Bagging and Boosting by figuring out clustering strategies using high dimensional datasets on each algorithm above. After the process of data cleaning in dataset, we have clustered the datasets and compared the summary of each to showcase the comparability of difference in their strategical values such as Clustering tendency, clustering quality and data driven approach for evaluating the number of clusters, Normalized Mutual Information (NMI) metric and provide an idea to choose the algorithm for clustering the data effectively. And as a result, Local Clustering Coefficient (LCC) with K-means clustering bunching method performs better than the other clustering algorithms and the results are reported.

Decision tree is a model to classify data based on labelled attribute values. This model is a supervised learning approach through which one can classify a new entry into an appropriate class. If we want to know the cause behind this classification then decision tree cannot provide the same. When we infer causes behind the classification then they will provide a rich knowledge for better decision making. Causal Bayesian Networks, Structural Equation Models, Potential Outcome Models are the some of the models that are used to get causal relationships. These models need experimental data. But it is not possible/ it is very expensive to conduct full experiments. So a model is needed to identify causes from effects from observational data rather than experimental data. In this paper a novel approach is proposed for causal inference rule mining which can infer the causes from observational data in a faster way and also scalable. Statistical tools and techniques named partial association test, correlation are used to develop the model. A new way of constructing a tree called Dimensionality Reduction Partial Association Tree (DRPAT) is introduced. Sometimes the existing causality cannot be extracted where low associated dimensions are involved in data and hiding the underlying causality and this model extracts causal association in case of hidden causality in data.. The model is applied on “Cardiovascular Disease dataset” sourced from Kaggle Progression System. The result is a Partial Association Tree. From this tree one can get a set of causal rules which can form a basis for better data analytics and then the better decision making.

Being one of the hot technologies of the era Augmented reality is exponentially developing various day to day activities and learning application is one of them. EduAR is an android application that overlays the virtual objects in a physical environment. EduAR as the title suggests it will be related to education and Augmented reality. Our project will be basically for preschool and kindergarten kids. In this application we are going to provide two main features, User will put camera on the particular word for alphabet and there will be a 3D visual of the particular word in the form of image it will be a static image but will be presented are displayed in a magical way as simplifying and visualizing something new leads to easier understanding. Another feature is that the user will put a camera on the particular object or image and then it will display on screen what object is it children can have fun using this application to explore new things. This application will mainly focus on the development of young children to help them prepare for preschool and beyond.

In this paper, our main aim is to create a Medicinal plant identification system using Deep Learning concept. This system will classify the medicinal plant species with high accuracy. Identification and classification of medicinal plants are essential for better treatment. In this system we are going to use five different Indian medicinal plant species namely Pungai, Jamun (Naval), Jatropha curcas, kuppaimeni and Basil. We utilize dataset contains 58,280 images, includes approximately 10,000 images for each species. We use leaf texture, shape, and color, physiological or morphological as the features set of the data. The data are collected by us. We use CNN architecture to train our data and develop the system with high accuracy. Several model architectures were trained, with the best performance reaching a 96.67% success rate in identifying the corresponding medicinal plant. The significantly high success rate makes the model a very useful advisory or early warning tool.

Agriculture not only provides food for humans, but it is also a major source of revenue for any nation. Millions of dollars are spent every year to protect rice crops from insects and pests that cause damage during harvest and storage. Early pest detection, which allows the crop to be protected from pest attack, is one form of crop protection. The best way to learn about the health of a crop is to examine it regularly. If pests are discovered, adequate steps may be taken to prevent the crop from suffering a major loss of yield. Early detection will help to reduce the use of pesticides and direct the pesticide selection process. It has grown into a large field of science, with a lot of work being done around the world to detect pests automatically. The typical way of inspecting the fields is with the naked eye. A farmer must manually search and assess over a vast landscape of fields, risking overlooking different affected areas and conducting thorough research across large lots. To analyse the entire area, several human experts are needed, which is both costly and time-consuming. This proposed system is mainly intended to develop an Intelligent IT-driven system using various Artificial Intelligence and Computer Vision Algorithms for precision farming, enabling the delivery of information directly to the farmer's phone, providing the details of damage localization, crop health, and needs for fertilizer and pesticide application.

In the field of material removal, metal cutting is one of the most important manufacturing methods. The parametric optimization of the turning mechanism is the subject of this article. Cutting speed, feed rate, and cut depth are specific input parameters. MINITAB 18 uses a L9 orthogonal array to design the combination of these parameters. Turning operations are based on the Design of Experiment are used to assess tool wear and surface roughness. The Taguchi method was used to design and optimize the experiment. ANOVA was used to assess which cutting parameters have a major impact on surface roughness and tool wear. To optimize surface roughness and finish, EN 31 is used as a workpiece and SNMG120408MS is used as a carbide cutting tools wear .Cutting speed (40, 60, and 90 m/min), feed rate (0.1, 0.15, and 0.2 mm/rev), and cut depth are the turning parameters (0.5, 0.75 and 1.0 mm). Arm wear at each cutting edge of the tool is determined by toolmaker microscope and surface roughness is measured by Talysurf  profilometer (Taylor Hobson Surtronic 3). Cutting speed is the most important tool parameter, according to the findings.

Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network.   So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network. But, owing to the unsuitable selection of features, the ML models which introduced previously suffer from misclassify errors. So, there arises a need to study the problem of feature selection more depth to predict the accurate traffic flow observation in the network. To overcome this problem, a new structure in machine learning (ML) is introduced. So, for thisa novel features selection metric CorrAUC is suggested. So, based on  this metric approach, a new feature selection algorithm CorrAUC is develop and design, it is based on wrapper technique to get features accurately by filtering to predict flow of traffic is suggested. Then, we applied multicriteria decision method called VIKOR which is used for validating the features selected for recognition the flow of traffic errors in the network. We estimate our approach by using the NSL-KDD dataset and three different ML algorithms.

This paper deals with development and application of Battery Pack for an Electric Kart. A Battery Pack is an integral part of an Electric Kart. This paper highlights the challenges in the development of a battery pack and its application. Since there is a gradual shift towards the electric vehicle from the traditional combustion engine vehicles, there is a requirement of research and improvement in the field of batteries used in an Electric Kart. This paper purely focuses on the development of a battery pack with nominal range and power output with phenomenal charging rate; keeping in mind the economic perspective of the power pack.

 
Most of the day-to-day activities are done using mobile apps, even the same for the farmers. The mobile apps have given many benefits to farmers starting from better land management judgements to quality yield. Farmers are using different types of apps to review the health of the crops during the crop cycle. Some of the mobile applications are developed to help the farmers in lots of ways like horticulture, crop management etc. Also, some mobile framer applications inform the farmers about the weather forecast, agricultural field opportunities, expert suggestions, answer to the questions, etc. Aloof, some of the apps also offer details related to the quality of soil, the utilization of fertilizer’s, etc.

Women are subjected to an increasing amount of harassment these days, which is troubling. The situation is extremely serious in both developing and developing countries. As a result, it poses a serious threat to women's empowerment as well as a country's fiscal development. We are developing IoT software and an Android app to make women's movement safer in this project. By pressing the device's emergency button, women will receive immediate and comprehensive safety assistance. In the event of an incident, this system will monitor the user's location in real-time and send it to a local police station and volunteer. This device will also provide the user with the location of the nearest safe zone. Furthermore, this interface can be used both online and offline. If the user does not have access to the internet, the computer can also be used to contact the nearest police station and volunteer assistance. Arduino uno, GPS, GSM, Bluetooth, and other components make up the system. The combination of both of these factors makes this product both inexpensive and simple to use.

The pollution caused due to the fire work is becoming one of the major problems in both environmental safety and human health. The greenhouse gases released during the burning of crackers causes global warming and eventually increase the temperature of the Earth’s atmosphere. The pollutants from burning the crackers cause health hazard in human being. The particulate matters in the dust such as PM2.5 and PM10 are easily inhaled by the human beings and affect the human health. So a new mixture which has less pollutant, particulate matter as compared with the existing mixture is formed with the help of Boron Carbide as fuel and Potassium Nitrate as the oxidizer. The newly formed mixture is completely Barium free and is much safer to handle than the Barium nitrate. The Boron carbide is implemented in the smoke flare and was found to have the performance as same as the existing composition. So the alternative for the Barium nitrate is proposed and various tests are done for the new mixture.