MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

&nbsp;

MIT World Peace University

School of Computer Engineering & Technology

Agriculture sector is a major contributor to global greenhouse gases (GHGs) emission and thus to anthropogenic climate change. In the proposed system, we used soil attributes, i.e., soil type, soil humidity, soil temperature, Ph value, soil moisture, and climatic attributes, i.e., temperature, humidity, wind speed, pressure, and location to analyze and predict the emission of greenhouse gases CO2 and CH4 from Pune, India. We used different regression techniques and deep learning model to analyze and predict the emission of CO2 and CH4 for different crops and season wise also. The result indicated that the decision tree regressor gives good result, Root Mean Square Error (RMSE) values 0.032930 and 0.026116 for emission of CO2 and CH4 as compared to other algorithm used. The deep learning model works best for 4 layers of sequential neural network. The RMSE values for number of epoch 1000 with different layers are 13.18, 7.87, and 10.36. Thus the effect of soil and climate attributes makes the difference in the greenhouse gas emission from agriculture field. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Engineering and Technology

Advances in Intelligent Systems and Computing

Effect of Soil and Climatic Attribute on Greenhouse Gas Emission from Agriculture Sector

Software Development Lifecycle (SDLC) is a systematic process used to achieve high quality software that meets customer requirements. During SDLC requirements, engineering plays an important role. Prioritisation helps to focus on the most important requirements in terms of importance, cost, penalty, time and risk. Stakeholders (users, developers) of the software product identify requirements. The two major activities of requirement engineering process are requirements classification and requirements prioritisation. Sometimes requirement mentioned by stakeholder can be of both types, i.e. functional and non-functional. So it is challenging to classify requirements separately in two different categories. There are many fundamental prioritisation techniques available to prioritise software requirements. In this paper, we have compared existing requirements prioritisation techniques based on ease of use, speed, scalability and accuracy. Our literature study suggests that the appropriate requirements prioritisation technique has to be selected that can help software developer to minimise the risk, penalty. In automating various tasks of software engineering, machine learning (ML) has shown useful positive impact. This paper discusses the various algorithms used to classify and prioritise the software requirements. The results in terms of performance, scalability and accuracy from different studies are contradictory in nature due to variations in research methodologies and the type of dataset used. Based on the literature survey conducted, we propose a new architecture that will use both types of datasets, i.e. Software Requirement Specifications (SRS) and user text reviews to create a generalised model. Our proposed architecture will attempt to extract features which can be used to train the model using ML algorithms. The ML algorithms for classifying and prioritising software requirements will be developed and assessed based on performance, scalability and accuracy. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Software Requirements Classification and Prioritisation Using Machine Learning

World University root node

Artificial Intelligence and Machine Learning Methods for Renewable Energy

The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative na{\"{i}}ve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB) and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard na{\"{i}}ve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative na{\"{i}}ve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory and execution time.

Fulltext

Journal of Big Data

Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

International Journal of Computer Science and Mobile Computing

Detecting Hate Speech and Offensive Language on Twitter using Machine Learning

International Journal of Computer Applications

Image and Signal Processing of Mel-Spectrograms in Isolated Speech Recognition

Digit Dataset Generation Using DCGAN: A ResNet Experimentation

Bi-Directional Chains of Neural Nets for Multi-Target Regression

Fruit classification is essential in various industrial settings, such as factories, supermarkets, and other places. Fruit classification may also be beneficial to persons with unique nutritional needs who utilize it to choose the proper fruits. Manual sorting was formerly used for fruit classification is time-consuming and requires continual human presence. Many fruit classification machine learning techniques have been proposed in the past. Deep learning may be a powerful engine for generating actionable results in today's reality because of its detection and classification abilities. As a result, a convolutional neural network was employed to construct an effective fruit classification model. It makes use of the fruits 360 dataset, which contains 131 different fruit and vegetable varieties. In this paper, we used three fruits, divided into three categories: good, raw, and damaged. The model was made in Keras. It had been trained for 50 epochs and had a 95% accuracy rate. © 2021 IEEE.

2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

Fruit Quality Recognition using Deep Learning Algorithm

With the growing population, the amount of waste generated by the public also increases drastically. Without a proper solution, waste is being thrown by passersby in open areas in the public. This leads to the accumulation of waste in random areas and it becomes tedious to clear up later on. This also becomes a growing hazard for the environment and the living organisms. It inhibits the growth of flora and fauna and becomes a breeding ground for numerous diseases and pests. This has a direct effect on the health of the population and may lead to permanent damage to the earth. This should hence be controlled as soon as possible to avert immense consequences. In today's world, many systems are being IoT automated. This helps reduce the amount of human effort that is required to operate and maintain systems which in turn reduces human error. With the involvement of IoT, a mass amount of data is also being produced in real-time and the proper interpretation of this data should be done. An interface that has ways to interpret and depict this data is very useful. This will help interpret and analyze the data and conclude various inferences that help in decision-making and future development. This interface should be simple and user-friendly so that people can operate it with minimal requirements or qualifications. This system provides a solution to waste management wherein the citizens of the country are encouraged to throw waste in the waste bins through incentivized technology. The users can find out suitable locations to throw away their waste using IoT components embedded in the dustbins that indicate the status of the dustbins in the locality and also calculate various other data. The Raspberry Pi enables the data to be collected which can then be sent to the cloud to be further processed over the cloud server. This data is then administered by the admin who then takes the appropriate decisions on when to deploy the garbage collecting trucks when suitable. © 2021 IEEE.