Abdul Salam Shah

Work place: Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

E-mail: shahsalamss@gmail.com


Research Interests: Artificial Intelligence, Image Compression, Image Manipulation, Image Processing, Data Mining, Data Structures and Algorithms


Abdul Salam Shah is currently doing postgraduate degree in computer science from SZABIST Islamabad Pakistan. He did his BS degree in computer science from Isra University Hyderabad, Sindh Pakistan in 2012. In addition to his degree he has competed short courses and diploma certificates in databases, cybercrime, networking and software engineering.
He has published articles in various journals of high repute. He is a young professional and he started his carrier in the Ministry of Planning, Development and Reforms, Islamabad Pakistan. His research area includes Machine Learning, Artificial Intelligence, Digital Image Processing and Data Mining.
Mr. Shah has contributed in a book titled “Research Methodologies; an Islamic perspectives,” International Islamic University Malaysia, in press.

Author Articles
Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network

By Fazli Wahid Rozaida Ghazali Muhammad Fayaz Abdul Salam Shah

DOI: https://doi.org/10.5815/ijitcs.2017.05.04, Pub. Date: 8 May 2017

In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

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An Appraisal of Off-line Signature Verification Techniques

By Abdul Salam Shah M. N. A. Khan Asadullah Shah

DOI: https://doi.org/10.5815/ijmecs.2015.04.08, Pub. Date: 8 Apr. 2015

Biometrics is being commonly used nowadays for the identification and verification of humans everywhere in the world. In biometrics humans unique characteristics like palm, fingerprints, iris etc. are being used. Pattern Recognition and image processing are the major areas where research on signature verification is carried out. Hand written Signature of an individual is also unique and for identification of humans are being used and accepted specially in the banking and other financial transactions. The hand written signatures due to its importance are at target of fraudulence. In this paper we have surveyed different papers on techniques that are currently used for the identification and verification of Offline signatures.

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Other Articles