Author (s) : DOI : 10.32692/IJDI-ERET/10.2.2021.2101

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1). Dhimesh P Parmar, Harivandna College, Rajkot, Gujarat, India

Abstract :

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Data science is secure in with supervision huge natural world of information to pull out major and logical outcomes/conclusion/design. It is a recently growing field that incorporates a range of exercises, for example, data mining and information research. It utilizes actions extend from mathematics, insight, and data innovation, Computer programming, information building, design recognition and learning, observation, and better processing. This article gives a definite attention on the topic of the various data science innovation.

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Author (s) : DOI : 10.32692/IJDI-ERET/10.2.2021.2102

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1). Sajid Mannan, Indian Institute of Technology Gandhinagar (IIT-Gn), Gandhinagar, Gujarat, India

Abstract :

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In this study an attempt has been made to calculate various fracture parameters from experimental results for different size beams under three-point bending. Analyzing the full field of displacement, strain, crack propagation and formation of Fracture Process Zone (FPZ) in notched plain concrete beams using Digital Image correlation. In addition, analysis of fracture parameters obtained from the Rilem Method of Hillerborge, Jenq and Shah, and Bazant Size Effect Model has been carried out and further validated with existing literature and it has been found in good agreement.

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Author (s) : DOI : 10.32692/IJDI-ERET/10.2.2021.2103

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1). T. Moses, Federal University of Lafia, Lafia, Nasarawa, NIGERIA
2). T. A. Badmos, Sheffield Hallam University, Sheffield, United Kingdom
3). R. Abdulkarim, Federal University of Lafia, Lafia, Nasarawa, NIGERIA

Abstract :

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Hadoop MapReduce has been the major computational paradigm for the analysis and exploration of big data. It facilitates concurrent processing of big data by splitting data into chunks and processing these data using commodity cluster processors. The processed data are aggregated from the multiple commodity clusters to return a consolidated output. Hadoop YARN has been a major enabler for this computation but its central resource manager is a bottleneck. Rack-aware resource management system developed to overcome this bottleneck decentralized the responsibilities of the resource manager by providing another layer in the architecture of Hadoop called Rack Unit Resource Manager Layer. This work, therefore, analyses the MapReduce operation between these two architectures to understand the behaviour of each data chunk (block). Wordcount operation was used for this analysis and the result obtained showed that the rack-aware system performed better as data grows bigger.

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Author (s) : DOI : 10.32692/IJDI-ERET/10.2.2021.2104

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1). V. H. Shukla, L.D. College of Engineering, Ahmedabad, Gujarat, India
2). H. S. Syed, GEC Bhuj, Bhuj, Gujarat, India
3). V. R. Shah, L.D. College of Engineering, Ahmedabad, Gujarat, India

Abstract :

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Industries are thriving in a developing country like India, and population growth, transportation congestion, and construction activity are all on the rise. As a result, a variety of air pollutants are released into the atmosphere, causing harm to individuals, plants, and property. The amount of air pollutants emitted from various sources that tend to stay in the atmosphere is influenced by the site's meteorological and topographical characteristics. As a result, it's vital to evaluate the ambient air quality, which can also be done using a variety of ambient air quality dispersion models. AERMOD is a state-of-the-art air quality dispersion software that is used all over the world. This review research explores several sources of air pollutants and their contributions to overall ambient air quality as monitored by continuous ambient air quality monitoring stations (CAAQMS). The usefulness of air quality dispersion modelling along with key features of the AERMOD software and even the types of input data needed to run the model, are also addressed. In particular, the AERMOD software's applications and performance for numerous air quality dispersion studies conducted in India have been critically examined. The role of default values as a model input (i.e. Albedo, Bowen, and Surface roughness) on the performance is a significant component explored in this review work & methods for determination is also been included. These default values differ from location to location, thus it's critical to determine out what they are before starting the AERMOD software. Adoption of these approaches can help the AERMOD model work more effectively in the Indian context.

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Author (s) : DOI : 10.32692/IJDI-ERET/10.2.2021.2105

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1). Vikas Sharma, Ganpat University, Ahmedabad,, Gujarat, India

Abstract :

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The security community all over the world has been confronting malicious programs for Windows- Based operating systems for the last few decades. However, as technological expansion took place, the adoption of embedded devices and IoT has exponentially increased leading to rapidly changing the malware landscape. Embedded devices are quite different than personal computers as they run on different architecture whereas personal computers are still dominating on x86 or x64 flavored architectures. The surge in the utilization of Linux or its variant has forced malicious actors to introduce “Linux Malwares”. There is presently no systematic study attempting to classify, evaluate, and understand Linux malware that we are aware of. The majority of resources on the topic are sparse reports often published as blog posts, while the few systematic studies focused on the analysis of specific malware families (e.g., the Mirai botnet) primarily through network-level behavior, leaving the main challenges of analyzing Linux malware unaddressed.

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