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

World University root node

Engineering and Technology

Fake news classification on twitter using flume, n-gram analysis, and decision tree machine learning technique

International Journal of Scientific Research in Engineering and Management (IJSREM)

AI Estimating Viable Locations for Onshore Wind Turbine Installations

Detection, and mapping of potholes in a precise and punctual manner is an essential task in avoiding road accidents. Today, roadway distresses are manually detected, which requires time and labor. In this paper, we introduce a system which uses deep learning algorithms and is integrated with smartphones to detect potholes in real-time. The user interface of the system is a smartphone application which maps all potholes on a route that the user is traveling. Simultaneously, deep learning object detection algorithm: Single Shot Multi-box Detector (SSD) looks for potholes using a mobile camera in the background. As soon as an unregistered pothole is detected by SSD, coordinates of the pothole are updated to the database in real-time. Accelerometer and gyroscope readings are continuously taken and assessed by a Deep Feed Forward Neural Network model to detect unregistered potholes. This dual mechanism of camera-based as well as accelerometer-gyroscope based detection not only cross validates detections but also provides stable results even if one mechanism fails. The pothole co-ordinates are rendered on the map user interface that can be accessed in the same application. This system with map/navigation feature as front end and two-fold deep learning pothole detection algorithm in backend is an efficient and a zero cost solution for real-time pothole detection.

IEEE Xplore

Deep Learning Approach to Detect Potholes in Real-Time using Smartphone

Tnm cancer stage detection from unstructured pathology reports of breast cancer patients

Building efficient Image Features with optimal dimension and high discriminative power is important in processing the digital information hidden inside the images. Image Retrieval systems use the feature vectors to retrieve similar images based on feature vector similarity. Image Feature Vector generation is important step in CBIR. Most of the CBIR use Linear Data structure for Feature Vector. In this paper the Hierarchical data Structure: Binary Tree is used for Feature vector creation in Image Retrieval Systems. Images in Spatial Domain are considered for Feature Vector Generation. Low-level Feature: Color Descriptors are used as Image Contents, to represents the image in feature vector form. Performance of the image retrieval is tested for Color Images. It has been observed that, along with the Dimensionally reduction, the proposed approach of Feature Vector generation, improves the discriminative power of Feature Vector. For RGB colorspaces the cascaded statistical features, when stores in hierarchical data structure, this provides the facility to perform the calculations in parallel manner also capable to keep discriminative power comparable w.r.t size of feature vector. © 2020 The Authors. Published by Elsevier B.V.

Fulltext

Procedia Computer Science

Feature Vector Creation Using Hierarchical Data Structure for Spatial Domain Image Retrieval

There is always a big influence of robotics and natural language processing in the field of automation. The current scenario of natural language processing (NLP) focuses on machine analysis and machine interpretation. By the term machine analysis, accuracy becomes an important phenomenon. So, it is named Augean that refers to higher accuracy for natural language processing which is difficult to attain involving the use of neural networks. Thus far named as Augean artificial neural network. It is one of the key branches of machine learning which is peculiar from the other branches speculating more towards artificial intelligence. NLP is now one of the key aspects in making machine learn and letting it speak. The current framework of these discusses more of deep learning. One of the biggest challenges is to make the machine learn and speak is known. Thus far processing becomes the most crucial part. Therefore, when it comes to processing there is no better idea than the brain to be involved. Now the current scenario is about making machine learn to the best of the accuracy and if there is something of great accuracy then it involves a real complex algorithm. The machine works possibly best only if the accuracy stays high and stepping on to the precision of work with better learning ability machines are remarkable. So, to frame this, artificial neural network and neuroscience have been used to make the model more robust increasing the accuracy with at least five percent to the previous models involved. © 2020 IEEE.

Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

Augean Artificial Neural Network

Signature analysis helps in analyzing and understanding individual’s personality. Graphology is the scientific technique that helps us predict the writer’s personality. Different types of strokes and patterns in writer’s signature are considered for predicting their personality trait. Social skills, achievements, work habits, temperament, etc. can be predicted by using the writer’s signature. It helps us in understanding the person in a better way. As signature is directly related and develops a positive impact on your social life, personal life as well as for your career it is essential to practice correct signature for good results. The main objective here is predicting authors personality trait based on features such as Skewness, Pen Pressure, Aspect Ratio, Margin, and the difference between the first and last letter of the signature. As your signature has a direct impact on any of your assets and career, the proposed system will also provide suggestions for improvement in the signature if needed. This research paper proposes an off-line signature analysis. We have created our own dataset for the analysis purpose. We have also provided them with some questionnaire to check the accuracy of the proposed system. © 2020, Springer Nature Switzerland AG.

Advances in Intelligent Systems and Computing

Personality trait with E-graphologist

Human Gait refers to motion accomplished through the movement of hand limbs. Gait analysis is a precise investigation of the human walking pattern using sensors attached to the body for recording body movements during activities like walking on a flat surface, treadmill and running. This paper addresses the analysis of human gait based on performance using various classification techniques involving Decision Tree, Random Forest and KNN algorithms. The paper highlights the comparison of these classification models based on various parameters using the RapidMiner Studio tool. The comparison is based on performance metrics and Receiver Operating Characteristic (ROC). The results show that the Random Forest algorithm performs better in classifying normal and abnormal gait. © Springer Nature Singapore Pte Ltd. 2020.

Human gait analysis based on decision tree, random forest and KNN algorithms

Age estimation is an interesting and challenging research area, gaining significant importance in the recent era and is employed in various applications, such as intelligent surveillance, face recognition, biometrics, and so on. Various techniques are employed in the literature for age estimation from the face images. This paper introduces the age estimation scheme from the face image, and estimation is done by defining a novel feature extraction strategy, named Local Transform Directional Pattern (LTDP). The database containing the input images has many unwanted regions, and thus, the Viola Jones algorithm detects the required face region. After detecting the face regions, active appearance model extracts the active appearance features, and the proposed LTDP extracts the texture features. The proposed LTDP feature extraction model modifies the existing Local Directional Pattern (LDP) with several other texture feature extraction models. After the feature extraction, the Cuckoo search based Deep Belief Network (CDBN) classifier estimates the age of the person from the face image based on the extracted features. The simulation results reveal that the proposed LTDP with the CDBN classifier achieved high performance with the values of 2.3416, 0.9803, and 0.9724 for MAE, AEO, and AEM, respectively.

Evolutionary Intelligence- Springer

Local transform directional pattern and optimization driven DBN for age estimation

Dynamic Textures are temporal extension of static textures. Texture is defined as an image with some repetitive pattern in it. Dynamic texture (DT) is nothing but video data with some stationary properties and some moving objects in it. DT segmentation is a broad branch of DT Analysis. Segmentation is separating disjoint regions in the image frames which have homogeneous properties such as texture, color, motion. An important role is played by it in the applications like forest fire detection, traffic density detection, crowd congestion detection before stampede, auto pilot airplanes. There are various approaches used for DT segmentation. In this paper, the approaches based on optical flow, local spatiotemporal technique (Local Binary Pattern) and Global spatiotemporal technique (Contourlet transform) are discussed. Each technique is used and modified or combined with some other technique by researchers. This paper gives an overview of all techniques along with variations in them and the benefits achieved by using them on DT dataset. Some experimental results are also presented.