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Sadaqat Jan

Bio: Sadaqat Jan is an academic researcher from University of Engineering and Technology, Lahore. The author has contributed to research in topics: Ontology (information science) & Mobile computing. The author has an hindex of 8, co-authored 44 publications receiving 197 citations. Previous affiliations of Sadaqat Jan include University of Engineering and Technology, Peshawar & Brunel University London.

Papers
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Journal ArticleDOI
TL;DR: Experimental results show that the proposed FE-SVR-mFFO forecasting framework outperforms benchmark frameworks like EMD-Svr-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO in terms of accuracy, stability, and convergence rate.

46 citations

Journal ArticleDOI
TL;DR: Simulation results illustrate that the proposed energy optimization model optimizes the performance of smart microgrid in aspects of operation cost, pollution emission, and availability compared to the existing models with/without involvement in hybrid DRPS and IBT.

42 citations

Journal ArticleDOI
TL;DR: A system where price is dependent variable which is predicted, and this price is derived from factors like vehicle’s model, make, city, version, color, mileage, alloy rims and power steering is proposed.
Abstract: This paper presents a vehicle price prediction system by using the supervised machine learning technique. The research uses multiple linear regression as the machine learning prediction method which offered 98% prediction precision. Using multiple linear regression, there are multiple independent variables but one and only one dependent variable whose actual and predicted values are compared to find precision of results. This paper proposes a system where price is dependent variable which is predicted, and this price is derived from factors like vehicle’s model, make, city, version, color, mileage, alloy rims and power steering.

37 citations

Journal ArticleDOI
15 Apr 2021-Energies
TL;DR: A novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation is proposed.
Abstract: Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

32 citations

Journal ArticleDOI
TL;DR: The results show that the cooperative network performs best when the relay is located in the middle of source to destination link, at lower SNR values, and the performance of the system is worst if the relay was located closer to the source than to the destination.
Abstract: This paper presents an analysis on the performance of single-relay and multiple fixed-relay cooperative network. The relay nodes operate in amplify-and-forward AF mode and transmit the signal through orthogonal channels. We consider maximal-ratio combining at the destination to get the spatial diversity by adding the received signals coherently. The closed-form moment-generating function MGF for the total equivalent signal-to-noise ratio SNR is derived. The exact expressions of symbol-error rate, outage capacity, and outage probability are obtained using the closed-form MGF for single-relay and multiple-relay cooperative network with M-ary phase shift keying M-PSK and M-ary quadrature amplitude modulation M-QAM over independent and non-identical Nakagami-m channels and Rician fading channels. The approximated closed-form expression of ergodic capacity is derived for both Nakagami-m and Rician fading channels. The performance of the system is analyzed at various relay locations. The theoretical results are then compared with the simulation results obtained for binary PSK, quadrature PSK, and 16-QAM modulation schemes to verify the analysis. Here, the expressions derived can be easily and more efficiently used to compute the performance parameters than doing Monte Carlo simulations. It is shown that cooperation is significant only for low K values for Rician by plotting cooperation gain versus K. The results show that the cooperative network performs best when the relay is located in the middle of source to destination link, at lower SNR values, and the performance of the system is worst if the relay is located closer to the source than to the destination. Copyright © 2013 John Wiley & Sons, Ltd.

24 citations


Cited by
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01 Jan 2011

284 citations

Book ChapterDOI
01 Jan 2005
TL;DR: The goal is to help developers find the most suitable language for their representation needs in the Semantic Web, which has a need for languages to represent the semantic information that this Web requires.
Abstract: being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web—known as the Semantic Web—which has been defined as “the conceptual structuring of the Web in an explicit machine-readable way.”1 This definition does not differ too much from the one used for defining an ontology: “An ontology is an explicit, machinereadable specification of a shared conceptualization.”2 In fact, new ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires—solving the heterogeneous data exchange in this heterogeneous environment. Here, we don’t decide which language is best of the Semantic Web. Rather, our goal is to help developers find the most suitable language for their representation needs.

212 citations

Journal ArticleDOI
TL;DR: In this paper, the magnetic properties of the nanocomposites were evaluated to determine whether they possessed sufficient magnetization for easy separation by an external magnet, and the unique features of the synthesized materials were investigated along with their application in the removal of heavy metal ions and dyes.
Abstract: A massive release of harmful substances especially heavy metal ions and dyes to the environment has been a major concern due to many people disregards the proper protocols in the waste management. The freshwater supplies are threatened and huge discharge of pollutants result from various anthropogenic activities may pose a major threat to the living organisms and negatively affect the ecosystem stability. This article reviews the development of magnetic iron oxide nanocomposites for removal of heavy metal ions and dyes from water. The highlight will be focused on current research activities for controlled size and dispersion of magnetic iron oxide nanoparticles within solid matrices including zeolites, silica, clays, carbon, activated carbon, graphene and graphene oxide. The magnetic properties of the nanocomposites will be evaluated to determine whether they possessed sufficient magnetization for easy separation by an external magnet. The unique features of the synthesized materials will be investigated along with their application in the removal of heavy metal ions and dyes. The advantages and limitations of the magnetic nanocomposites will be highlighted to determine their adsorption ability. The effect of various parameters such as pH, contaminants concentration, adsorbent dosage, contact time and temperature will be summarized to identify the best condition for effective pollutants removal.

120 citations