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

School of Computer Engineering & Technology

Probability of spare components adequacy is very crucial within the method of conventional operation of companies. This paper focuses on the military’s order forecasting of standby parts which has been paid much attention in military supply line which ultimately has the impact on nationwide guard finances. Formerly used time series analysis method needs more upgrading and precision so the machine learning ensemble techniques like Bagging and Boosting are proved to be useful for enhanced accuracy and suitability. Inventory dataset related to military like the most used aircraft, period during which aircraft saw the most runs, types of weapons used, etc. can be forecasted by applying ensemble learning. The principal advantage of using Ensemble techniques is that the model’s randomness is reduced and thus perks up the precision for stipulated forecasting.

World University root node

Engineering and Technology

Universal Review Journal

Demand Forecasting of Anti-Aircraft Missiles Using Ensemble Techniques.

Now-a-days demand forecasting is used in many countries for military applications such as spare parts of aircraft and for improving budget efficiency. In supply chain management demand forecasting is a major issue. Currently, time series technique is used to demand forecast but this technique resulted in lack of accuracy and improvement in accuracy is needed. So this paper focused on comparing the features which leads to improvement in the accuracy and propose a system for demand forecasting of spare parts of anti-aircraft missiles, which are based on machine learning and neural networks such that equipment's are properly utilized and alongwith that budget is also maintained. We have compared the existing features with the new features added and applied algorithms and looked upon at the accuracy. Experimental results proves that the new features added gave higher accuracy. Here, we also present an end-to-end boosting system called XGBoost and Multi-layer Perceptron. These new techniques were compared with traditional Machine Learning techniques. This experiment is conducted on the Vietnam War dataset. © 2019 IEEE.

Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019

Analysis on Machine Learning Algorithms and Neural Networks for Demand Forecasting of Anti-Aircraft Missile Spare Parts

A farmer uses different agriculture practices to improve the crop yield generating the greenhouse gases (GHGs). The soil management practices are very important in the greenhouse gas emission from agriculture sector. In this paper we tried to find the greenhouse gas CH4 and CO2 emission from agriculture using soil attributes i.e. soil moisture, soil temperature, humidity, pH values, and N-P-K values. We used different machine learning algorithm such as Decision table, SOM regression, ZeroR, Randomforest, Multilayer Perception and Multiple Linear regression for comparison purpose. We collected soil sample using different sensors from the farm. We have used Mean absolute error(MAE) and Root mean squared error (RMSE) as a coefficient of correlation with 10 fold cross validation. The result shows that for CH4 eq and CO2 eq the SOMregressor performs better than other classifiers with perfect positive correlation whereas the ZeroR classifier gives negative correlation for CO2 eq and CH4 eq emission. Also the OLS statistics of multiple linear regression result shows that R squared value 0.786 means 78% predicted values are similar to actual reading of CH4 eq emission. © 2019 IEEE.

2019 IEEE Pune Section International Conference, PuneCon 2019

Effect of Soil Attribute on Greenhouse Gas Emission from Agriculture using IoT

Human interaction and carrier of feelings among humans are accomplished mainly through five senses, such as touch, smell, taste, audio, and Visual. Considering Visual sense, images and videos are important gradients in day-to-day life. It can elevate/depress mood of a person. Video Aesthetics improves user satisfaction. In this study of mood detection, we are demonstrating an approach which is automatically assessed the aesthetics of videos, with emphasis on detecting the mood of videos. With the rising popularity of DSLR camera, Mobile dual camera, the amount of Visual data available on the internet is expanding exponentially. Some of the videos and pictures are aesthetically pleasing and beautiful to human eyes. But there are some videos as well as photos which are uninteresting and of low-quality. This paper demonstrates a simple but powerful method to classify videos into pleasing and non-pleasing categories. Further, our aesthetic quality assessment will find the mood of aesthetically pleasing video (i.e. video representing a happy video or sad or fear, etc), that reflect on a person's mood. © 2019 IEEE.

2019 Global Conference for Advancement in Technology, GCAT 2019

Detecting Mood of Aesthetically Pleasing Videos Using Deep Learning

In recent times, the advancements in Brain-Computer Interface has not only been instrumental in achieving its fundamental purpose of aiding disabled people, but also in creating novel applications like playing games without physical controls or operating home appliances merely by the power of your brain. The electrical activity generated in the brain is measured by an EEG device after which the collected raw data undergoes through various steps, namely: Signal acquisition, Data Preprocessing, Feature Extraction, and Classification. This paper helps the reader in understanding the different algorithms and methods used in each of these processes. A detailed survey of various applications of BCI using different feature extraction and classification techniques is done. Finally, we have compiled all the current issues which hinder the efficiency of BCI systems. © 2019 IEEE.

ICCIDS 2019 - 2nd International Conference on Computational Intelligence in Data Science, Proceedings

A review of recent trends in EEG based Brain-Computer Interface

Wireless Sensor Networks (WSNs) are instrumental facilitators in the development of the Internet of Things (IoT). In the smart world of IoT, energy efficient data gathering is an essential requirement to extend the sustainability of the network. In WSNs, Data Aggregation Trees (DATs) are employed for energy efficient data gathering. While DAT gathers data, it is imperative that the network provides enhanced Quality of Service (QoS). Constructing a DAT that caters to the requirements of the application results in improved QoS. This work explores QoS of DATs with Network Lifetime (NL) as a parameter for QoS. QDD, an algorithm to enhance QoS of DATs with Deterministic Network Model(DNM) is proposed. In DNM, any two sensors either communicate or they do not. However, in practical scenarios the communication between a pair of sensors is probabilistic and is represented using the Probabilistic Network Model (PNM). QDP, an algorithm to enhance QoS of DATs with PNM is proposed. Simulation results show the effectiveness of the proposed algorithms and demonstrates improved QoS through DAT path refinements.

2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019

Data Aggregation Trees with QoS in Sensor Networks

Countering Cyber Attacks and Preserving the Integrity and Availability of {\ldots}

Study on Query-Based Information Extraction in IoT-Integrated Wireless Sensor Networks

Diabetic retinopathy (DR) mostly affects the people as a result of diabetes and it leads to blindness. Diabetic retinopathy (DR) affects the eyes as a result of increased blood glucose levels. Among people in the 70-year-old age group, 50% of deaths are associated with diabetes. Detection of diabetes at a early stage and taking a proper treatment can reduce vision loss among the patients. Once DR symptoms are recognized, the severity of the disease must be assessed in order to prescribe the correct medication. There are five stages of severity of diabetic retinopathy, Mild Non Proliferative Diabetic Retinopathy (NPDR),Moderate NPDR, Severe NPDR, Proliferative Diabetic Retinopathy(PDR) and No DR(Diabetic Retinopathy).This paper summarizes DR detection methods and problems.