We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.

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One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental data is time and resource consuming in practice, necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novel approach is presented in this work, where a multi-ion-sensing emulator is designed to explain the response of an ion-sensing array in a mixed-ion environment.

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Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spec- troscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments.

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Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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399 Views

At the ground level, a map is a detailed and accurate two-dimensional representation of natural and human- made features of the linear objects. These object can be in the form of a circle, rectangle or any combination of geometrical shapes. For domain analysis, attributes are used by merging the geometry and the real map. These attributes are defined by the practical study of the raw map data. One of the practical problems of maps related to the identification of junctions in a roundabout has been considered.

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Description:

This repository contains the datasets used as part of the OC2 lab's work on Student Performance prediction and student engagement prediction in eLearning environments using machine learning methods.

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The PD-BioStampRC21 dataset provides data from a wearable sensor accelerometry studyconducted for studying activity, gait, tremor, and other motor symptoms in individuals with Parkinson's disease (PD).In addition to individuals with PD, the dataset also includes data for controls that also went through the same study protocol as the PD participants. Data were acquired using lightweight MC 10 BioStamp RC sensors (MC 10 Inc, Lexington, MA), five of which were attached to each participant for gathering data over a roughly two day interval.

Instructions: 

Users of the dataset should cite the following paper:

Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong, Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma, "A real-world study of wearable sensors in Parkinson’s disease". Submitted.

where an overview of the study protocol is also provided. Additional detail specific to the dataset and file naming conventions is provided here.

The dataset is comprised of two main components: (I) Sensor and UPDRS-assessment-task annotation data for each participant and (II) demographic and clinical assessment data for all participants. Each of these is described in turn below:

I) Sensor and UPDRS-assessment-task annotation data:

For each participant the sensor accelerometry  and UPDRS-assessment-task annotation data are provided as a zip file, for instance, ParticipantID018DataPDBioStampRC.zip for participant ID 018. Unzipping the file generates a folder with a name matching the participant ID, for example, 018, that contains the data organized as the following files. Times and timestamps are consistently reported in units of milliseconds starting from the instant of the earliest sensor recording (for the first sensor applied to the participant).

a) Accelerometer sensor data files (CSV) corresponding to the five different sensor placement locations, which are abbreviated as

   1) Trunk (chest)                  - abbreviated as "ch"

   2) Left anterior thigh           - abbreviated as "ll"

   3) Right anterior thigh        - abbreviated as "rl"

   4) Left anterior forearm      - abbreviated as "lh"

   5) Right anterior forearm    - abbreviated as "rh"

   Example file name for accelerometer sensor data files:

   "AbbreviatedSensorLocation"_ID"ParticipantID"Accel.csv

   E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv, lh_ID018Accel.csv, and rh_ID018Accel.csv

   File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for    each measurement and corresponding triaxial accelerometry relative to the sensor coordinate system.

   Column 1: "Timestamp (ms)"             - Time in milliseconds

   Column 2: "Accel X (g)"                      - Acceleration in X-direction (in units of g = 9.8 m/s^2)

   Column 3: "Accel Y (g)"                      - Acceleration in Y-direction (in units of g = 9.8 m/s^2)

   Column 4: "Accel Z (g)"                      - Acceleration in Z-direction (in units of g = 9.8 m/s^2)

b) Annotation file (CSV). This file provides tagging annotations for the sensor data that identify, via start and end timestamps,     the durations of various clinical assessments performed in the study.   

   Example file name for annotation file:

   AnnotID"ParticipantID".csv

   E.g. AnnotID018.csv 

    File format for the annotation file: Comprises of four columns

   Column 1: "Event Type"                      - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of                                                                 two queries -  medication status and MDS-UPDRS assessment body locations

   Column 2: "Start Timestamp (ms)"     - Start timestamp for the MDS-UPDRS assessments

   Column 3: "Stop Timestamp (ms)"      - Stop timestamp for the MDS-UPDRS assessments

   Column 4: "Value"                               - Responses to the queries in Column 1 - medication status (OFF/ON) and                                                                  MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)

   II) Demographic and clinical assessment data

For all participants, the demographic and clinical assessment data are provided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzipping the file generates a CSV file named Clinic_DataPDBioStampRCStudy.csv.

File format for the demographic and clinical assessment data file: Comprises of 19 columns

Column 1: "ID"                                                                              - Participant ID

Column 2: "Sex"                                                                            - Participant sex (Male/Female)

Column 3: "Status"                                                                        - Participant disease status (PD/Control)

Column 4: "Age"                                                                            - Participant age

Column 5: "updrs_3_17a"                                                              - Rest tremor amplitude (RUE - Right Upper Extremity)

Column 6: "updrs_3_17b"                                                              - Rest tremor amplitude (LUE - Left Upper Extremity)

Column 7: "updrs_3_17c"                                                              - Rest tremor amplitude (RLE - Right Lower Extremity)

Column 8: "updrs_3_17d"                                                              - Rest tremor amplitude (LLE - Right Lower Extremity)

Column 9: "updrs_3_17e"                                                              - Rest tremor amplitude (Lip/Jaw)

Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off"  - Rest tremor amplitude during OFF medication assessment                                                                                                         (ordering similar as that from Column 5 to Column 9)

Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on"   - Rest tremor amplitude during ON medication assessment

For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:

Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23, 2129-2170, doi:10.1002/mds.22340 (2008)   

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BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.

Instructions: 

If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.

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195 Views

Data set of 26/11 Mumbai attack is based on Mumbai Terrorist Attacks 2008 India Ministry of External Affairs Dossier and News reports. 10 terrorist operated in India distributed in five sub-groups, simultaneously 3 other person comes in light as per report those were having in continue touch with these terrorist from Pakistan and giving them instructions.                                                                                  

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This dataset contains 4669820 ratings from 1499238 users to 351109 movies on the imdb.com website. This data is collected from reviews (https://www.imdb.com/review/rw0000001/). Each row in this dataset is as follows:

userID, movieID, rating, review date

For example : 

ur18238764, tt2177461, 9, 22 January 2019

Instructions: 

Use the following code to read the dataset :

import numpy as np

dataset = np.load ("Dataset.npy")

print (dataset [0])

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