Novus-Publishers

Dr.Ahmed SA

PhD (CS)
Editor in Chief (European Journal of Advanced Trends in Computer Science and Engineering-Novus)
Novus Publishers, Italy
Trentino, Italy
Email:sajjadarid@gmail.com

PC member of Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022) U.S.A Editorial Board Member American Journal of Artificial Intelligence U.S.A

Welcome!

I am a researcher at Unicam working with Professor Dr. Knut Hinkelmann (FHNW, Switzerland) & Professor Dr. Flavio Corradini (UNICAM). My research is in computer science. My PhD research topic was “Combining Text Classification and Fact Checking to Detect Fake News . I received my Ph.D. in Computer Science from the International School of Advanced Studies at the University of Camerino, Italy in 2022.

Recent Work

1. Ahmed S., Hinkelmann K., Corradini F. (2022) Fact Checking: An Automatic End to End Fact Checking System. In: Lahby M., Pathan AS.K., Maleh Y., Yafooz W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-90087-8_17

2. Ahmed S., Balla K., Hinkelmann K., Corradini F. (2021) Fact Checking: Detection of Check Worthy Statements Through Support Vector Machine and Feed Forward Neural Network. In: Arai K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_37.

3. Ahmed, S., Hinkelmann, K., & Corradini, F. Development of Fake News Model Using Machine Learning through Natural Language Processing.World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering Vol: 14, No:12, 2020.

4. Ahmed, S., Hinkelmann, K., & Corradini, F. (2019). Combining machine learning with knowledge engineering to detect fake news in social networks-a survey. In Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). Stanford University, Palo Alto, California, USA, March 25-27, 2019.

During the research period, I collaborated on other research works which resulted in the following publications:

1. Ghosh A., Ahmed S. (2021) Shared Medical Decision-Making and Patient-Centered Collaboration. In: Dutta G., Biswas A., Chakrabarti A. (eds) Modern Techniques in Biosensors. Studies in Systems, Decision and Control, vol 327. Springer, Singapore. https://doi.org/10.1007/978-981-15-9612-4_10

2. Khan, M. A., Hong, L., & Ahmed, S. (2020). Hand Gesture Recognition of Dumb Person using one against All Neural Network (IJCSIS) International Journal of Computer Science and Information Security, Vol. 18, No. 04, April 2020.

3. Ghosh, A., Liaquat, S., & Ahmed, S. (2020). Healthcare-Internet of Things (H-IoT) can assist and address emerging challenges in healthcare. International Journal of Science and Innovative Research, Vol. 1, No. 02, Dec 20

Selected Projects

Combining Machine Learning with Knowledge Engineering to Detect Fake News in Social Networks

Ahmed S. a,  Hinkelmann K.,a,b  Corradini F.,a

aSchool of Science and Technology, Computer Science Division, University of Camerino, Via S. Agostino 1, 62032 Camerino; e-mail: ahmed.sajjad@unicam.it

bSchool of Business, FHNW, University of Applied Sciences and Arts, Northwestern Switzerland, 4600 Olten; e-mail: knut.hinkelmann@fhnw.ch

To answer this question our idea is to combine learning from data and engineered knowledge in order to combat fake news detection in social media. A new text classification algorithm approach shall be developed which will classify the text as soon as news is published online into the classes’ fake, non-fake, unclear. These days the most actively researched classifier is Support Vector Machine (SVM) that can classify the text [2]. After text classification the next step for identification of fake news is stance detection, which categorizes the news into four categories: agree, disagree, discuss and not related. For this purpose we will apply different algorithms to check the stance of the other social sites. In next step we will apply fact checking that will refine the results. While classification can be based on cross validation using deep learning methods based on text and metadata like source, author, topic and claim wise [3], fact checking uses engineered knowledge in order to analyse the content of the text and compare it to known facts. At the end the three sub results will be combined in order to differentiate the fake and non fake news in social networks.

Public Sector Experience

July 10, 2015- July 10, 2016 (Manager IT)

April 28, 2010- June 2016 (BPS-17) (Administrator IT)

October 25, 2009- April 28, 2010 (BPS-16) (Data Manager)

August 21, 2007- October 29, 2009 (Back Up Team Lead)

University Experience

March 2014- December 2015             Lecturer/AP (Computer Science Department, University of Lahore, Blue Area, Islamabad)

December 2010- December 2015   Online Exam Superintendent (Virtual University of Pakistan)

 

 

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