A. M. Mohammad

Jawaharlal Nehru Technological University, Hyderabad

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Jawad Nagi

Dalle Molle Institute for Artificial Intelligence Research

Keem Siah Yap

Universiti Tenaga Nasional

Sieh Kiong Tiong

Universiti Tenaga Nasional

Behrouzian Nejad

Abdelwadood Mesleh

Al-Balqa Applied University

Majdi Ahed

Information Systems Laboratories (United States)

Syed Khaleel Ahmed

Signal Processing (United States)

C. Javad Chaharlang

Anita Rani

Dr. B. R. Ambedkar National Institute of Technology Jalandhar

Syed Khaleel Ahmed

Universiti Tenaga Nasional

Cooperative Institutions

Universiti Tenaga Nasional

Technical University of Malaysia Malacca

Tenaga Nasional Berhad (Malaysia)

Dalle Molle Institute for Artificial Intelligence Research

Università della Svizzera italiana

University of Malaya

Graduate School USA

Xiamen University Malaysia

National Institutes of Biotechnology Malaysia

King Abdulaziz University

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Non-Technical Loss analysis for detection of electricity theft using support vector machines

Jawad Nagi A. M. Mohammad Keem Siah Yap Sieh Kiong Tiong Syed Khaleel Ahmed

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Non-Technical Loss (NTL) analysis for electric utilities using a novel intelligence-based technique, Support Vector Machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behavior. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The SVM based approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behavior that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.

Consumption

10.1109/pecon.2008.4762604

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Detection of abnormalities and electricity theft using genetic Support Vector Machines

Jawad Nagi Keem Siah Yap Sieh Kiong Tiong Syed Khaleel Ahmed A. M. Mohammad

Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities.

10.1109/tencon.2008.4766403

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Citations (169)

Study on Learning Techniques for Text Classification

A. M. Mohammad Behrouzian Nejad Bassel Yasser Sayed Mohsen Hashemi C. Javad Chaharlang

Data mining is a one of most popular technologies to information management. One of the most important our data, is text document. We can sort and classify these text documents by data mining techniques. Text classification is a technique to sorting text documents. Basic steps in text classification are preprocessing of documents, feature extract/ selection, selects learning algorithm and evaluation. When we want to classify text documents using computer systems and machine learning techniques, one of the most important steps in text classification is selecting a learning algorithm. In this paper we review some effectiveness learning algorithm researches and show review results of these in a table form.

Data pre-processing

Document classification

Feature (linguistics)

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An Enhanced Twitter Corpus for the Classification of Arabic Speech Acts

International Journal of Advanced Computer Science and Applications (2020)

Majdi Ahed H. Bassam A. M. Mohammad

Twitter has gained wide attention as a major social media platform where many topics are discussed on daily basis through millions of tweets. A tweet can be viewed as a speech act (SA), which is an utterance for presenting information, hiding indirect meaning, or carrying out an action. According to SA theory, SA can represent an assertion, a question, a recommendation, or many other things. In this paper, we tackle the problem of constructing a reference corpus of Arabic tweets for the classification of Arabic speech acts. We refer to this corpus as the Arabic Tweets Speech Act Corpus (ArTSAC). It is an enhancement of a modern standard Arabic (MSA) tweet corpus of speech acts called ArSAS. ArTSAC is more advantageous than ArSAS in terms of its richness of annotated features. The goal of ArTSAC is twofold: Firstly, to understand the purpose and intention of tweets which act in accordance with the SA theory, and hence positively influencing the development of many natural language processing (NLP) applications. Secondly, as a future goal, to be used as a benchmark annotated dataset for testing and evaluating state-of-the-art Arabic SA classification algorithms and applications. ArTSAC has been put in practice to classify Arabic tweets containing speech acts using the Support Vector Machine (SVM) classification algorithm. The results of the experiments show that the enhanced ArTSAC corpus achieved an average precision of 90.6% and an F-score of 89.6%. Substantially it outperformed the results of its predecessor ArTSAC corpus.

Utterance

Modern Standard Arabic

Benchmark (surveying)

Assertion

10.14569/ijacsa.2020.0110325

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SUPPORT VECTOR MACHINE TEXT CLASSIFIER FOR ARABIC ARTICLES: ANT COLONY OPTIMIZATION- BASED FEATURE SUBSET SELECTION

A. M. Mohammad Abdelwadood Mesleh

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Citations (10)