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;

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MIT World Peace University

Engineering and Technology

Social media has been an unavoidable part of our life over the years. As it is getting popular, the number of social media bots are also increasing. Social media bots are the artificial agents who imitate as a human on social media. They are intended to like, retweet the posts which eventually can tamper with the genuineness of the trend. They can also be a menace to democracy as they can falsely influence people. Social media bots can be used for cyberbullying, terrorist activities, gaining fame, spreading wrong information, restricting freedom of speech, spamming. To detect social media bots on Twitter, we utilized metadata of Twitter profiles and applied a unique feature selection method, and also explored the power of ensemble learning to make a robust classifier. © 2021 IEEE.

World University root node

School of Computer Engineering & Technology

Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021

Enhanced Twitter bot detection using ensemble machine learning

A construction project usually involves different types of activities and has number of factors affecting it. Such situations demand rigorous monitoring and fluent coordination on the project. The final output of any project must be that it is delivered successfully. In this study, the researchers have focused on application of a conceptual model based on KPIs and project constraints to monitor a real estate building project in Pune. To predict the project delivery success factor (PDSF) of a 13 storeyed commercial building, two additional parameters, project performance index (PPI) and project monitoring and coordination index (PMCI), have been defined and used. Data has been collected from on-site observations, collecting authentic documents related to the particular project, meeting various involved stakeholders, interviewing them and making a note of their opinion and expertise. It has been clearly observed through the results that the PDSF for the real estate project under study is less. Therefore, the particular project has been considered as a possible failure, and various measures have been recommended to improve the PDSF. © 2021, Springer Nature Singapore Pte Ltd.

School of Civil Engineering

Lecture Notes in Civil Engineering

Monitoring of a Construction Project in Its Execution Phase Using Project Delivery Success Factor (PDSF)

The initial costs of a building are quite small in comparison to the life cycle costs, as it is estimated that they represent less than 30% of the total life cycle cost of a building. Accordingly, if we consider integration of the life cycle cost of a building into the estimation of investment values, then we would have a better and clearer idea of how much our total operation costs are during and after construction process. Did an overview of Life Cycle Assessment and Modelling tools and BIM integrated LCC evaluations. Used BIM objectives by Krieder for to generate Life Cycle Cost Analysis of the Residential Building. Findings of this study demonstrate the opportunities to manage and estimate a reliable and accurate information on a building’s life cycle in real time by considering each element and its components into the calculation, which may change the metrics for a real estate economic evaluation in order to achieve sustainable and efficient property investment. The adoption of the building information modelling to generate relative LCCA databases directly benefits the real investment cost calculation for investors. © 2021, Springer Nature Singapore Pte Ltd.

Use of BIM for Study of Life Cycle Cost Analysis of Residential Complex

Indian climate which primarily consists of four seasons and the majority of agriculture in India is practised in monsoon which is rainfed agriculture. The lack of irrigation makes India a potent place for the experiment of efficient water irrigation. Internet of Things (IoT) have emerged has ubiquitous technology which is being used in many fields. Machine Learning has proven to be helpful in data-intensive tasks. IoT and machine learning would prove helpful in agriculture. This paper proposes a system based on the research of machine learning and IoT to increase the efficiency in irrigation. With the help of data gathered through sensors and using machine learning algorithm the amount of water the needed for irrigation is calculated and used for irrigation. © 2021, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Smart Irrigation Using Decision Tree

The maintaining of the Quality of Service(QoS) parameter in wireless sensor networks (WSN) is becoming a challenging task. The limitations are like achieving QoS parameters in a distributed network, constraints in sensing parameters, natural disasters and battery life of sensor nodes need to overcome. Here in this paper few of the QoS parameters are analysed to increase the lifetime of sensor networks by partitioning a network into various sectors. A novel optimized sectoring scheme is proposed for optimal election of sector head (SH) for a given network. A Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P-CSO-EERSS) is also proposed in this paper. The use of prediction algorithms is for early detection of node lifetime to improve the overall lifespan of a network. Along with this QoS parameters such as packet delivery ratio and energy consumption are analysed and compared with existing clustering algorithms. According to implementation results, it has been shown that the prediction based CSO-EERSS algorithm returns better QoS parameters as compare to existing cluster schemes.

Fulltext

Journal of Physics: Conference Series

Analysis of QoS Parameters using Prediction Algorithms on CSO-based Energy-Efficient Reliable Sectoring Scheme in WSN

International Journal of innovative Science and Research Technology 6 (Issue {\ldots}

Survey Paper on Ambulance Tracking Systems

School of Electronics & Communication Engineering

Advances in Communication and Computational Technology

Evaluation of SC-FDMA Physical Link Using USRP

Computed Tomography (CT) images of the patient's cranium are widely used in the diagnosis of hemorrhage. They are reviewed by highly skilled professionals to find the existence, location and type of hemorrhage. This process is complex, labor-intensive and requires lot of time. The massive progress in machine learning techniques and availability of high computational power has made it possible to detect such hemorrhages automatically. The paper provides survey of recent deep learning approaches for automatic detection of such acute intracranial hemorrhage and its subtypes. Further the experimental results using windowing technique inspired by practical radiologist's approach and relevant research papers is provided to verify its efficacy as a pre-processing step before feeding DICOM images to the network. Transfer learning approach is used to detect hemorrhage types. The open source Kaggle platform is utilized for computational resources and for carrying out the experimental study. Some key evaluation metrics and loss functions addressing the data imbalance underlying with the problem statement are also presented. The paper concludes with addressing key insights into open issues in the proposed domain. © 2021 IEEE.

2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021

A survey on deep learning for intracranial hemorrhage detection

Single point incremental forming (SPIF) process is an advanced dieless sheet metal forming process in which the requirement of a dedicated punch-die setup is eliminated. The dedicated punch-die setup is replaced by a universal blank holding fixture, a punch (or tool), and a backing plate. It has a variety of applications ranging from automotive to biomedical fields. But its limitations such as inability to form steeper wall angle and high geometric error in single-stage restricts its application in sheet metal industries. To overcome this, multistage SPIF process is an alternative to achieve larger wall angles. Formability in multistage SPIF process is increased by providing intermediate stages. Various methodologies have been suggested to enhance the profile accuracy, however, it still remains the major issue. In the present paper influence of process variables namely feed rate, number of forming stages and pitch size on geometric accuracy and surface roughness of formed part is investigated. Taguchi L18 orthogonal array is used for design of the experiments. From the analysis of variance (ANOVA), it is found that the number of stages and pitch size have a significant influence on geometric accuracy and surface roughness. Since feed rate is an insignificant parameter. So a higher feed rate can be used to reduce forming time. Further, a mathematical model is developed to predict the geometrical accuracy and surface roughness of the formed part. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.