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 kmers based  detection for bacterial pathogenicty 

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 kmers based  detection for bacterial pathogenicty 

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Group Members

1.       Yussuf Yakubu                                  P-COM01050/17

2.       Ahmad Yamin Abdul Rahman      P-COD0005/18(R)

3.       Addy Suyatno Hadisuwito             P-COD0011/18(R)

4.       Bello Hassan                                    P-COD0014/18(R)

5.       Abdullahi Aminu Kazaure              P-COD0019/18(R)

| Important Concept | Real World Examples
| Dependent Variable | Unique Signatures
| Independent Variable | Kmer length
| Extraneous Variable | Type of genomes
| Nuisance Variables |
| Control Group | Pentamer words
| Experimental Group | Hexamer words, 11-mer words
| Treatment |
| Confounding |

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Data security breaches in an organization

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Data security breaches in an organization

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Name: Mohd Majid Bin Abu Bakar                        Matric No. P-COM0089/18

Name: Abubakar Mu’azu Ahmed                           Matric No. P-COD0010/18

Name: Abdullahi Idris Na’umme                                          Matric No. P-COD0015/18

Name: Sivaguru Satyaseelan                                   Matric No. P-COM0083/18

Name: Huang Nan                                                     Matric No. P-COM0133/18

Research Problem: Data security breaches in an organization

Dependent Variable - Organization Information Security

e.g. Security of computers and systems, Operating system security, Network security in an organizations

Independent Variable - Information Security Management Standard

e.g. threats, risk

Extraneous Variable - Environment aspect

e.g. natural disaster

Control Group - Having an experiment from both level, security attacker and security defender

Experimental Group -What type of experiment will be done

e.g. scope, environmental constraints, time constraints, purpose

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Automatic Reviews Annotation Using Machine Learning Approach

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Automatic Reviews Annotation Using Machine Learning Approach

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1. Syazwan Syafiqah Binti Sukri P-COM0002/18(R)
2. Sharmilawathy A/P Paramesvaran P-COM0034/18
3. Vivian Lee Lay Shan P-COM0004/17(R)
4. Abdul Salam Bin Waqar Ahmed S-COM0004/18(R)
5. Mohamad Amar Irsyad Bin Mohd Aminuddin P-COD0003/18(R)

Research problem : Manual annotation of reviews (positive/negative) are time-consuming and costly. Therefore, it should be done using automatic approach. However, there are issues/gaps exist on automatic approach where it could be further studied and improved. This research focus are to reduce the time of automated review annotation, and also ensure the quality is on par with manual annotation review.

Dependent variable : Time and accuracy of automated review annotation.

Independent variable : The Machine Learning algorithm used.

Experimental group : Amazon.com reviews dataset.

Practical application : Annotate reviews on e-commerce platform.

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The Prediction An Outbreak of Dengue Fever Cases

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Smart Traffic Control System using Radar Sensors

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Smart Traffic Control System using Radar Sensors

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1. Idrees Fazili P-IPD0012/18 (R)
2. Haruna Aminu Rawayau P- COM0053/18 (MM)
3. Ho Jia Jie P-COM0060/18 (MM)

Dependent Variables: Radars, Vehicles
Independent Variables: Class of Vehicles, Drivers
Extraneous Variables: Speed Breakers, Road Repair
Nuisance Variables: Accidents, Road Blocks
Control Groups: Latent hours traffic
Experimental Group: Peak Hours Traffic
Treatment: Class of Vehicles, Drivers
Confounding: Extreme Weather, natural disasters