Computer Network Attack Detection Using Enhanced Clustering Technologies

The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.

Teaching Cross-Cultural Competence in a Smart Machine Age

The business world is in a state of flux due in part to the advent of disruptive technologies such as artificial intelligence, smart robots, and nanotechnology. The dawn of this ‘Smart Machine Age' has significant implications for business education, which will need to be transformed with a new focus on imparting knowledge, skills, and abilities suitable for the new workplace environment. Among the skill sets that are in highest demand according to employers is cross-cultural competence (CC). Scholarly interest in CC has grown exponentially in recent years and there is an emerging consensus that it is best taught through non-traditional pedagogies centered on experiential learning. This chapter explores the efficacy of international service learning as a tool for teaching CC, with a focus on an innovative social entrepreneurship project undertaken at Florida International University. The project involves a partnership between a student organization, an NGO, and a women's self-help group in India.

Development of smart machine for sorting of deceased onions

Today, we are thinking to raise Farmer’s income through various means and measures. Implementation of new crop patterns, technology inclusion and promoting the eshtablishment of numerous agro processing industries will play a major role in agriculture sector. The labour issue is also one of the main concerns in many of the agricultural activities. In this paper we propose a technological evolvement in onion detection process, where we apply image processing and sensory mechanism to identify sprouted and rotten onions respectively. This will yield to quick, accurate and prompt supply of goods to the market, irrespective of lack of consistent but costly manpower. The efficiency of this prototype in identifying the sprouted onions with the help of camera is observed to be upto 87% and also the response of Gas sensing system in detecting rooten onions under prescribed chamber dimensions is analysed and obtained encouraging results.

An In-Depth Analysis of the Various Factors Influencing Industry 4.0 and its Impact on the Overall Performance of the Industrial Sector to Achieve Sustainable Development

<p class="0abstract">In the last two years, the entire world has been confronted with the emergence of pandemic Covid 19, which poses several challenges to the entire human race. Covid 19 has been forced to adopt new innovative technology. To survive in this precarious situation; most of the organization has adopted new innovative technology to meet the expectations of the market and the demands of consumers. In this study, the primary goal is to identify the various factors that have an impact on Industry 4.0 while also evaluating the impact of Industry 4.0 on the overall performance of the industrial sector. An exploratory research approach is used by researchers to discover the characteristics and relationships of new research phenomena. This approach is particularly useful when researchers are looking for answers to the questions of what and how they came to be discovered. This study demonstrates that the use of Industry 4.0 technologies will increase the effectiveness and efficiency of organizations and industries. This is beneficial because organizations and industries need to develop and survive in this competitive era. Adoption of smart machine and smart product applications will become more common with the widespread use of information and communication technology for all aspects of business operations and activities.</p>

Classification of Grain s and Quality Analysis u sing Deep Learning

There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.

Context-Based Resilience in Cyber-Physical Production System

Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method

To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.