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

Localization technologies have their own challenges dependent on the applications and surrounding environment. Few additional applications and open challenges of IoT node localization in the smart world are introduced in this chapter. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

World University root node

School of Electronics & Communication Engineering

SpringerBriefs in Applied Sciences and Technology

Localization in Smart Applications

Traffic congestion due to the increasing number of vehicles is an alarming problem globally and aggravating day by day. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Node Localization for Smart Parking Systems

Nickel based superalloys belong to a special class of high performance alloys that feature large amounts of nickel. The uniqueness of superalloys lies in their design features, most notably mechanical strength, durability, etc. Inconel 718 (IN718) is a nickel based superalloy that exhibits high tensile and impact resistant properties, along with good high temperature corrosion resistance. Selective Laser Melting (SLM) is additive manufacturing (AM) technology aimed at melting and fusing metal powders using high power density lasers to produce precision functional parts. The accuracy and functional characteristics of manufactured parts are highly dependent on process parameters. The processing parameters that control the SLM process comprise laser power (P), hatch spacing (HS), exposure time (t), and border power (BP). This work primarily focuses on finding the combined effect of these process parameters on the surface roughness (SR) and ultimate tensile strength (UTS) of IN718 manufactured by SLM using the design of experiments (DOE).

School of Mechanical Engineering

Fulltext

Journal of Electrochemical Science and Engineering

<a href="https://pub.iapchem.org/ojs/index.php/JESE/article/view/1286">Relationship modelling for surface finish for laser-based additive manufacturing</a>

Forming is the process in which deformation of the material takes place in a requisite die cavity without any failure. During this, bi-directional stretching and compression of the material takes place which results in defects necking, cracking and wrinkling. The challenges are different when same process is carried out at micro level with thin sheets below 100 microns. It has become imperative for many engineering fields to manufacture micro-sized components, due to growing miniaturization. It is also observed that the mechanical behavior of thin sheets differs greatly at the micro-level. Forming limit curve (FLC) is one way by which the formality of material can be assessed. The FLC can be plotted using three approaches—experimentally, empirically, and numerically. In this research, FLC is plotted experimentally for an ultra-thin Titanium sheet of 50&nbsp;µm. A newly designed tool with hemispherical punch of 4&nbsp;mm diameter was used for experimentation according to ASTM-2218-14 standard using samples of Uni-Axial, Intermediate Uni-Axial, Bi-Axial, Intermediate Bi-Axial, and Plane Strain paths to obtain limit strains. The objective of this research is to plot FLC with microstructural study to assess the behavior of Titanium Grade-II (TiG-II). For Numerical simulation M–K model was applied. The Numerical results and experimental results achieved are in good agreement. Micro structural study reveals phase identification and grain size assessment.

International Journal on Interactive Design and Manufacturing (IJIDeM)

<a href="https://link.springer.com/article/10.1007/s12008-022-00903-3">Novel product design of tool for investigating formability with microstructural study of bio-material titanium grade-II thin foils</a>

Data can be turned into easily understandable format and relevant business information with data visualization. Nowadays, there is more use of data visualization in business intelligence. Datasets can be visualized in a variety of visual insights using a different dynamic or interactive approach. This paper focuses on Microsoft Power BI operations and interactive display of educational institution databases with multiple modules. It uses live dashboards, interactive reports, charts, graphs, etc. to help users build significant business insight quickly and effectively. Selecting categories, values and fields make visualizations more accurate and gives desired results. Features like slicer and Q&A help to analyze, find out results quickly on larger datasets. Applying filters on visuals based on data features and set range to get insights from large datasets makes it easier. To determine relationship between features, OLAP visuals are created and compared to Power BI based on field values. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

School of Computer Engineering & Technology

Lecture Notes in Networks and Systems

Effective Use of Visualization Techniques for Educational Institutional Data Analysis

In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level (BGL). Often in healthcare it is observed that the dataset is imbalanced. Therefore, an Ensemble Approach using imbalanced dataset techniques synthetic minority over-sampling technique and adaptive synthetic oversampling methods with different evaluation methods like train-test, K-fold, stratified K-Fold and repeat traintest were used. This ensemble approach was implemented on diabetic dataset using K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB) and logistic regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and random forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and random forest had least average traintest time. Thus, the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.

Indonesian Journal of Electrical Engineering and Computer Science

Machine learning ensemble approach for healthcare data analytics

Pneumonia detection and recognition have been one of the major challenges, and the machine learning community has been trying to tackle. Pneumonia is identified in X-ray images by the virtue of haziness in the lung region created due to the air sacs filled with fluid or pus. As pneumonia affects around 7% of the world’s population and kills over 700,000 children annually, the research in this field has become more prominent. In severe cases of COVID-19, people also get pneumonia. Earlier attempts using CNN, ChexNet, ensembles of transfer learning models have been carried out to solve this problem. However, work in this field has not been keeping up with the advancements in neural network happened in past few years. In this work, a 15-layer CNN architecture called CXR-15 is proposed. The performance of the architecture was tested on a dataset with 5856 images and compared with various existing architectures. CXR-15 outperformed most of the existing architectures used for pneumonia detection like ChexNet, Xception, InceptionResNetV2, VGG16, EfficientNet-B5, CNN as feature extractors and MobileNetV2 by achieving an accuracy of 95.2%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

CXR-15: Deep Learning-Based Approach Towards Pneumonia Detection from Chest X-Rays

Though Light gauge steel framing is widely employed construction technique, LGS framing membes cladded with factory cast ferrocemnent panels is a novel form of construction. Use of factory cast ferrocement panel as cladding material is an improvisation over the attempts of using gypsum boards plasterboards, steel sheathing, and wooden panels. In the present work, project cost and project duration for G + 1 Building constructed in Reinforced concrete (RCC) and in LGS-ferrocement composite form have been compared. The sequencing of project activities and estimation of project duration of G + 1 building in both the forms has been performed on commercially available project planning software (MSP 2010). A selected G + 1building has been constructed employing LGS-ferrocement composite form at Pune, Maharashtra in year 2021–2022. The estimated project cost and duration is validated using the data for the construction of building at Pune, Maharashtra. Employing LGS-ferrocement form not only results in multi-fold reduction of project cost and duration but also guarantees sustainability by marginalizing carbon foot print of the construction when compared to RCC form. © 2022

School of Civil Engineering

Materials Today: Proceedings

Analysis of LGS- ferrocement composite construction technology as a cost-effective &amp; sustainable alternative to RCC

Cancer which begin to grow uncontrollably in the lungs is known as lung cancer. Lung cancer is second deadliest disease in the world. In this paper, we have implemented convolutional neural network model for detection of cancerous nodules from CT (computed tomography) scans of different infected patients. for experimentation dataset is collected from cancerimagingarchive website and performed preprocessing steps to make better fit for our model. further we split dataset into 70% training and 30% testing. we compare performance of our CNN(convolutional neural network) model with pretrained models like VGG16.at the end we have concluded with challenges,research gaps and future scope of our system. © 2022 IEEE.

7th International Conference on Communication and Electronics Systems, ICCES 2022 - Proceedings

Detection of Lung Cancer using CT Scans with Deep Learning Approach

Abstract- Motorcycles are one of the most popular types of automobiles worldwide. If our nation alone is taken into account, approximately 37 million two-wheelers are sold each year, and the number is growing day by the day. Motorcycles, mopeds, scooters (geared and non-geared), and other vehicles fall under this category. These cars are driven by people ranging in age from 18 to over 60. When travelling in such vehicles, a person must maintain a fixed posture for an extended period of time with little room for adjustment. Due to design and budgetary restrictions, only minor alterations may be made to meet the customer's needs. Motorbikes are prone to higher vibrations than four-wheelers due to uneven road conditions, vibrations caused by the vehicle itself, and less vibration dampening than four-wheelers. In addition to bad or unpleasant riding posture, vibration can cause discomfort, tension, and eventually pain in a specific muscle group or joint. This pain can be greatly reduced by using an ergonomically suited bike position.Keywords- Anthropometry, Motorbike, Riding Posture, Discomfort