International Journal of Computer and Applications

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Advancement in IoT based application has become the state-of-the art technology among the researcher due to the availability of Internet everywhere. To make the application more user friendly, web based and android based technologies have gained their importance in this cutting edge technology. In this paper, smart energy efficient home automation system is proposed that can access and control the home equipments from every corner of the world. For this system, Internet connectivity module is attached to the main supply unit of the home system which can be accessed through the Internet. For wireless connectivity, the static IP address is used. Home automation is based on multimodal application that can be operated using voice recognition command of the user using the Google Assistant or through a web based application. Thus, main objective of this work is to make our home automation system more secure and intelligent.

Smart Energy Efficient Home Automation System Using IoT

The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organisation. To realise these efficiencies emerging technologies such as Internet of Things (IoT) and Cyber Physical Systems (CPS) will be embedded in physical processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, manufacturing facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organisations must be able to work with big data technologies to meet the demands of smart manufacturing. However, as big data is a relatively new phenomenon and potential applications to manufacturing activities are wide-reaching and diverse, there has been an obvious lack of secondary research undertaken in the area. Without secondary research, it is difficult for researchers to identify gaps in the field, as well as aligning their work with other researchers to develop strong research themes. In this study, we use the formal research methodology of systematic mapping to provide a breadth-first review of big data technologies in manufacturing.

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Big data in manufacturing: a systematic mapping study

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Electromyography (EMG) is a technique that studies the electrical activity of the muscle. EMG is the basic component of Myo armband. The MYO ARMBAND is a device which can be used by humans to interact with computers. The myo armband involves the interaction of devices in VE (virtual environment). Working and characteristics of Myo are studied and analyzed. This paper surveys the work on visual analysis of gestures by using Myo armband and the input from keyboard and mouse. We have also performed two tests where participants are allowed to play a game, first by using keyboard and other by using MYO. Discussion and conclusion are also written at the end of this paper.

Evaluating and exploring the MYO ARMBAND

The insurance industry has a giant role in the sustainable economic growth of any country. With an increase in the number of insurance buyers, it has become an absolute necessity for an insurance company to have a detailed claim analysis system in place. Claim Analysis is performed by insurance companies to distinguish between fraudulent and genuine claims. Apart from that, Claim Analysis can also be used to understand the client strata in a much better way and implement the results further during the underwriting and acceptance/denial stage of policy enrollment. The main objective of this research work is to identify meaningful and decisive factors for claim filing and acceptance in a learning context through exploratory data analysis (EDA) and feature selection techniques. Also, machine learning algorithms are applied to the datasets and are evaluated using performance metrics.

Application of machine learning and data visualization techniques for decision support in the insurance sector

Smart home is a term that is commonly used to refer to homes where the appliances, lightning, air-conditioning, TVs etc. are capable of communicating with each other and can be controlled remotely according to a predefined schedule or via some kind of interface. In this project presents a Home automation system using Wireless Fidelity as communication interface. The idea behind this project was to develop a home automation system which is a cheaper alternative to commercial options of home automation and at the same time could be seamlessly integrated with the commercial products. Also the interface of the system should be as simple and easy to learn as possible so that even the elderly and disabled people are able to use it. The system could be controlled using an android application or Google Assistant. The android application is used to communicate with the firebase database and update its values, this in turn enables us to control the various sensors and electrical appliances in home. The project uses Node MCU ESP 8266 12 E as the controller and also as the module for wireless communication. Various sensors like MQ6, MQ 135, DHT 11 etc. have been used to take reading of the environment around the house and to keep a watch on it. Also Firebase has been used as the database to keep record of readings of the various sensors.

Implementation model of Wi-Fi based Smart Home System

Physical interaction in order to provide commands or gain access to a computer system is now a history. Voice or speech stimulated systems are a part of modern Smartphone culture. Automatic Speech Recognition is an important application of artificial intelligence. This paper provides a brief description of what automatic speech recognition is its various types and a overview of how the process works. After a precise review of Hidden Markov Model (HMM) & Mel Spectrum Cestrum Coefficient (MFCC), this paper discusses about the history of this technology, future aspects and scope. Applications of the technology in various fields are also discussed.

Digital life assistant using automated speech recognition

Twitter right now gets around 190 million tweets(little content based Web posts) a day, in which individualsshare their remarks with respect to an extensive variety ofsubjects. An expansive number of tweets incorporatesentiments about items and administrations. Notwithstanding, with Twitter being a moderately new wonder, these tweets areunderutilized as a hotspot for assessing client supposition andhave lead specialists to think about the likelihood of their abuseso as to recognize concealed information. Hence, two territoriesare pulling in more enthusiasm for the examination group, thefeeling mining and assessment investigation. We to perform anassessment examination of general's conclusions mined fromthe well known smaller scale blogging site Twitter. The realaccentuation of this paper is set on assessing precision ofvarious machine learning calculations for the errand of twitternotion investigation.

Seema Rawat

Papers

Evaluating and exploring the MYO ARMBAND

Application of machine learning and data visualization techniques for decision support in the insurance sector

Implementation model of Wi-Fi based Smart Home System

Digital life assistant using automated speech recognition

Analysis of Various Machine Learning Algorithms for Enhanced Opinion Mining Using Twitter Data Streams