Sajjad Mohsin

Bio: Sajjad Mohsin is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Facial recognition system & Artificial neural network. The author has an hindex of 20, co-authored 71 publications receiving 869 citations. Previous affiliations of Sajjad Mohsin include Muroran Institute of Technology & university of lille.

A Review on Demand Response: Pricing, Optimization, and Appliance Scheduling

Abstract: The evolution of conventional electric grid into Smart Grid (SG) has enabled utilities as well as consumers to reap fruits due to its time varying price mechanisms. The utilities can acquire benefits by improving stability of grid, lessening blackouts and brownouts, knowing better their consumers power needs and not investing into new infrastructures. On the other hand consumer can also reduce electric bills, gain incentives by installing renewable energy sources and exporting energy to the main grid and attain improved services from utility. Demand Response (DR) is one of the most cost effective and reliable techniques used by utilities for consumers load shifting. In this paper, we are presenting a review of several DR techniques with a specific view on pricing signals, optimization, appliance scheduling used and their benefits. A comprehensive performance comparison is also prepared with the help of multiple criteria of SG paradigm.

6th International Conference on Ambient Systems, Networks and Technologies, ANT 2015 and the 5th International Conference on Sustainable Energy Information Technology, SEIT 2015 A Review on Demand Response: Pricing, Optimization, and Appliance Scheduling

Abstract: The evolution of conventional electric grid into Smart Grid (SG) has enabled utilities as well as consumers to reap fruits due to its time varying price mechanisms. The utilities can acquire benefits by improving stability of grid, lessening blackouts and brownouts, knowing better their consumers power needs and not investing into new infrastructures. On the other hand consumer can also reduce electric bills, gain incentives by installing renewable energy sources and exporting energy to the main grid and attain improved services from utility. Demand Response (DR) is one of the most cost effective and reliable techniques used by utilities for consumers load shifting. In this paper, we are presenting a review of several DR techniques with a specific view on pricing signals, optimization, appliance scheduling used and their benefits. A comprehensive performance comparison is also prepared with the help of multiple criteria of SG paradigm. c

Brain Image Enhancement - A Survey

Abstract: Brain image enhancement, Examination, Conception and investigation permit measurable exploration and conception of medical images of various modalities such as MEG, EEG, PET, MRI, CT or microscopy, to name a few. The basic purpose of enhancement operation is to analyze the brain images precisely in order to effectively diagnose and examine the diseases and problems. Brain imaging is a subfield of medical image processing. The field basically deals with handling the functions and actions taken in the brain. Brain imaging provides a way to investigate and determine brain related diseases in an efficient and effective manner. Enhancement of brain images is a vast field in dealing with these images. The basic objective of this study is to evaluate and discuss different techniques and approaches proposed in order to handle different brain imaging types. The paper provides a short overview of different methods presented in the prospect of brain image enhancement.

Intelligent Image Retrieval Techniques: A Survey

Abstract: In the current era of digital communication, the use of digital images has increased for expressing, sharing andinterpreting information. While working with digital images, quite often it is necessary to search for a specific image for aparticular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of imagesbut it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbasedsearching task becomes extremely complex when the number of images is in the millions. To deal with thesituation, some intelligent way of content-based searching is required to fulfill the searching request with right visualcontents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficientand robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers whoconducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.

SDBATS: A Novel Algorithm for Task Scheduling in Heterogeneous Computing Systems

Abstract: A heterogeneous computing system (HCS) efficiently utilizes the heterogeneity of diverse computational resources interconnected with high speed networks to execute a group of compute intensive tasks. These are typically represented by means of a directed acyclic graph (DAG) with varied computational requirements and constraints. The optimal scheduling of the given set of precedence-constrained tasks to available resources is a core concern in HCS and is known to be NP-complete problem. Task prioritization has been a major criterion for achieving high performance in HCS. This paper presents a SD-Based Algorithm for Task Scheduling (SDBATS) which uses the standard deviation of the expected execution time of a given task on the available resources in the heterogeneous computing environment as a key attribute for assigning task priority. This new approach takes into account the task heterogeneity and achieves a significant reduction in the overall execution time of a given application. The performance of the proposed algorithm has been extensively studied under a variety of conditions on standard task graphs from Graph Partition Archive as well as on some real world application DAGs such as Gaussian Elimination and Fast Fourier Transformation application DAGs. Our results show that SDBATS outperforms well known existing DAG scheduling algorithms in terms of schedule length (make span) and speedup.

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Abstract: Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth.

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Abstract: The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

Data-Driven Techniques in Disaster Information Management

Abstract: Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

Abstract: The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

A review of unsupervised feature selection methods

Abstract: In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.