Faisal Shehzad

COMSATS University Islamabad | CUI · Department of Computer Science

Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by predicting and considering the underlying motivations and short-term goals of consumers. From a technical perspective, various sophisticated neural models were recently...

In session-based recommendation settings, a recommender system has to base its suggestions on the user interactions that are observed in an ongoing session. Since such sessions can consist of only a small set of interactions, various approaches based on Graph Neural Networks (GNN) were recently proposed, as they allow us to integrate various types...

Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious...

Non-technical losses (NTLs) are one of the major causes of revenue losses for electric utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are employed to detect NTLs. The existing studies are mostly concerned with tuning the hyperparameters of ML/DL methods for efficient detection of NTL, i.e., electricity the...

In this research article, we tackle the following limitations: high misclassification rate, low detection rate and, class imbalance problem and no availability of malicious or theft samples. The class imbalanced problem is severe issue in electricity theft detection that affects the performance of supervised learning methods. We exploit the adaptiv...

In this synopsis, the first solution introduces a hybrid deep learning model, which tackles the class imbalance problem and curse of dimensionality and low detection rate of existing models. The proposed model integrates benefits of both GoogLeNet and gated recurrent unit. The one dimensional EC data is fed into GRU to remember periodic patterns. W...

Data science is an emerging field, which has applications in multiple disciplines; like healthcare, advanced image recognition, airline route planning, augmented reality, targeted advertising, etc. In this thesis, we have exploited its applications in smart grids and financial markets with three major contributions. In the first two contributions,...

For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU)....

In this paper, two supervised learning models based solutions are proposed for Electricity Theft Detection (ETD). In the first solution, Adaptive Synthetic Edited Nearest Neighbor (ADASYNENN) is used to solve class imbalanced problem. For feature extraction, Locally Linear Embedding (LLE) technique is utilized. Moreover, Self-Attention Generative A...

In this research article, we tackle the following limitations: high misclassification rate, low detection rate and, class imbalance problem and no availability of malicious or theft samples. The class imbalanced problem is severe issue in electricity theft detection that affects the performance of supervised learning methods. We exploit the adaptiv...

In this presentation, i explain about artificial neural network, gradient descent problem, chain rule, gradient exploding and vanishing problems, drop layers, weight intialization techniques, weight optimization techniques (gradien descent, adagrad, etc) to reduce to loss value, global minima, local minima and etc.

Traditional Electronic Business (E-Business) is a process of exchanging goods and services in digital form where payment is done using electronic payment system. In traditional E-Business, banks and financial institutions are used as Third Party (TP) which has many drawbacks. The proposed model is about e-business using Internet of Things (IoT), wh...

Traditional Electronic Business (E-Business) is a process of exchanging goods and services in digital form where payment is done using electronic payment system. In traditional E-Business, banks and financial institutions are used as Third Party (TP) which has many drawbacks. The proposed model is about e-business using Internet of Things (IoT), wh...

During the process of charging, electric vehicle’s location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without...

The data sharing is the claim of actual scholars datasets to share and reuse in the future from any domain. The rise of blockchain technology has to increase universally and enhancement in share and reuse of scholars datasets. Despite there are numbers of security management frameworks for share data securely. However, those frameworks is a central...

During the process of charging, electric vehicle's location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without...

The data sharing is the claim of actual scholars datasets to share and reuse in the future from any domain. The rise of blockchain technology has to increase universally and enhancement in share and reuse of scholars datasets. Despite there are numbers of security management frameworks for share data securely. However, those frameworks is a central...