Siddharth Swarup Rautaray

Assessment of Object Detection Using Deep Convolutional Neural Networks

Detecting the objects from images and videos has always been the point of active research area for the applications of computer vision and artificial intelligence namely robotics, self-driving cars, automated video surveillance, crowd management, home automation and manufacturing industries, activity recognition systems, medical imaging, and biometrics. The recent years witnessed the boom of deep learning technology for its effective performance on image classification and detection challenges in visual recognition competitions like PASCAL VOC, Microsoft COCO, and ImageNet. Deep convolutional neural networks have provided promising results for object detection by alleviating the need for human expertise for manually handcrafting the features for extraction. It allows the model to learn automatically by letting the neural network to be trained on large-scale image data using powerful and robust GPUs in a parallel way, thus, reducing training time. This paper aims to highlight the state-of-the-art approaches based on the deep convolutional neural networks especially designed for object detection from images.

Deep Learning Approaches for Detecting Objects from Images: A Review

Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.

Feedback analysis using big data tools

With the ever increasing man-machine interaction, automation of process and decline in hardware and software cost, the amount of digital data generated and used is increasing day by day. The big data referred here is the massive amount of digital data generated in each and every second in structured, semi-structured and unstructured format throughout the world. This emerging field of big data analytic has driven the researcher worldwide toward design, development and implementation of various tools, technologies, architecture and platforms for analyzing the huge volume of data generated day to day. Big data consist of data sets which is difficult for legacy database management system to analysis. This paper details some analysis like feedback analysis, sentiment analysis and word-count. Feedback are important for the system enhancement, finding loop holes and as well as for proper work distribution. Feedback is valuable information that will be used to make good decision. Feedback is important not only when it highlights weaknesses but also for strengths. If analysis of feedback is done in wrong way then the result of analysis will also be wrong. As a result, the pattern identified will also be incorrect thus making the whole system incorrect as a whole. We will be implementing this proposed system for feedback analysis using Map-Reduce framework for processing large data set and for storage we will use Hadoop.

Name node performance enlarging by aggregator based HADOOP framework

Due to the increased market competition increased data management and analysis has landed as in an era that requires further optimization data management and analysis. Big data technologies like apache HADOOP provide a frame work for parallel data processing and generation of analyzed results. MAPREDUCE method is used for analysis of data using various data analysis algorithms like clustering, fragmentation and aggregation. As per the HADOOP architecture the data received from client is distributed to various data node by the name node and it is the responsibility of name node to track the task being performed by a data nodes through a task-tracker, The presented proposal aims to reduce the burden on name node in the HADOOP architecture by providing the assistance through aggregator node which act as interface between the name node & data node.

Real time financial analysis using big data technologies

Due to advancement in Science and Technologies there are enormous amount of data available on internet. A large volume of structured, semi-structured and unstructured data is being created at a very rapid speed every day from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Many people are not so much aware of different profitable and economical alternatives before using their money for goods or services. They are not so intelligent that they can quickly compare and judge that which product or service is better. The presented paper proposed a recommended system for management and utilisation of three components of salary i.e. saving, investment and expenditure. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommended system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Apache Mahout is used for analysing the data and implementation of the recommender system.

Performance of elasticsearch in cloud environment with nGram and non-nGram indexing

The fact that technology have changed the lives of human beings cannot be denied. It has drastically reduced the effort needed to perform a particular task and has increased the productivity and efficiency. Computers especially have been playing a very important role in almost all fields in todays world. They are used to store large amount of data in almost all sectors, be it business and industrial sectors, personal lives or any other. The research areas of science and technology uses computers to solve complex and critical problems. Information is the most important requirement of each individual. In this era of quick-growing and huge data, it has become increasingly illogical to analyse it with the help of traditional techniques or relational databases. New big data instruments, architectures and designs have come into existence to give better support to the requirements of organizations/institutions in analysing large data. Specifically, Elasticsearch, a full-text java based search engine, designed keeping cloud environment in mind solves issues of scalability, search in real time, and efficiency that relational databases were not able to address. In this paper, we present our own experience with Elasticsearch an open source, Apache Lucene based, full-text search engine that provides near real-time search ability, as well as a RESTful API for the ease of user in the field of research.

Improvising name node performance by aggregator aided HADOOP framework

Due to the increased market competition increased data management and analysis has landed as in an era that requires further optimization data management and analysis. Big data technologies like apache HADOOP provide a frame work for parallel data processing and generation of analyzed results. MAPREDUCE method is used for analysis of data using various data analysis algorithms like clustering, fragmentation and aggregation. As per the HADOOP architecture the data received from client is distributed to various data node by the name node and it is the responsibility of name node to track the task being performed by a data nodes through a task-tracker, The presented proposal for “Improvising Name Node Performance By Aggregator Aided HADOOP Framework” aims to reduce the burden on name node in the HADOOP architecture by providing the assistance through aggregator node which act as interface between the name node & data node.

Performance Analysis of Vision based Adaptive Hand Gesture Recognition System for Human Computer Interaction

The hand gestures based interactive interfaces have made the man machine interaction much more efficient. Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from the simple action of using our fingers to point at and using hands to move objects around to the more complex expressions for the feelings and communicating with others. Naturalistic and Intuitiveness of the hand gesture has been a great motivating factor for the researchers in the area of HCI to put their efforts to research and develop the more promising means of interaction between human and computers. This paper describes the design and implementation of an Adaptive Hand Gesture Recognition System using computer vision and gesture recognition techniques which develop a relatively economic input medium. The uniqueness of the system is its adaptability to integrate as an interface for different applications through different users and user modified gestures. The modeling of gestures has been done for recognition through matching the feature of defects present in the hand with the assigned gestures. The analysis of the system has been performed based on the different qualitative and quantitative parameters for the evaluation of user friendliness of the system towards different applications.

Big Data Approach for Epidemiology and Prevention of HIV/AIDS

Nowadays, the disease is spreading and becoming noxious to the society inattentive of hospitalization that is present. Toxic diseases are the disorder by organisms, such as bacteria, viruses, fungi, or parasites, which happened in a normal body. Some toxic syndromes pass from one individual to another individual, some are transferred due to animals bite or insects, and others may happen by consuming contaminated water or food or by getting exposed to the organisms which are present in the environment. AIDS becomes a rapidly spreading and turning the life to death disease. HIV spreads from one individual to another individual in the population, in many different ways that may be due to semen and blood. The study of disease is called pathology, which includes the study of cause. This paper mainly focuses on the prediction of disease like HIV/AIDS using supervised learning system.

A Big Step for Prediction of HIV/AIDS with Big Data Tools

Human activity is quickly transforming an ecosystem. How this transformation collision health of humanity, whose health is at risk, and the extent of the associated disease burden are relatively new subjects within the area of environmental health. HIV/AIDS, tubercular, and malaria are three major global public health an expression of intent to injure and cause substantial morbidness, undeadliness, not positive socioeconomic impact and nonstandard. The HIV/AIDS is actual of an occurrence disease to proceed with to be an important global challenge of health, and actual, to bear HIV-1 virus burden testing is more and more needed at the point of care (POC). The presence of Big Data is everywhere. It is not actually data, but is a concept which actually explains about the gathering of data, organizing the data, analyzing the data and getting information out of the data. More applications are created everyday to extract the value from it which is professional and practical. The use of Big Data technologies in enterprise data warehouse and business intelligence results in better business insights and decisions. Now, Big Data analytics recently used in the point-of-care delivery and disease penetration. Big Data analytics tools are essential and useful tools, which gives strength to companies to analyze entire data related to their customers and the flea market in which they perform. As this data holds a large amount of information concerning the specific type, commodity, client service satisfaction, and client sentiment, many companies have taken the use of Big Data analytics tool.