Papers by Imran Ahmed
In recent years, demand side management (DSM) techniques have been designed for residential, indu... more In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and en... more In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.
— In the prevailing law and order situation video surveillance have widespread applications from ... more — In the prevailing law and order situation video surveillance have widespread applications from public places to monitoring and security. In this paper a modular approach has been proposed to detect multiple events in videos. We have divided these events into three broad categories i.e. intrusion, loitering and slip and fall. The proposed approach is divided into primary and secondary analysis. Videos for surveillance have to be passed through the primary analysis, which can be used as an input for the secondary analysis. The proposed system achieved higher accuracy (90 %) than state of the art (85 %) for the aforementioned events. The results have been verified using publically available benchmark datasets.
— This research work introduces a simple yet effective method for brain tumor detection using pro... more — This research work introduces a simple yet effective method for brain tumor detection using proposed dataset of 1500 images. There are different types of brain tumor; among the existing we have considered four different types i.e. CNS Lymphoma, Glioblastoma, Meningioma, and Metastases. The four major steps in the proposed method are pre-processing, segmentation, post-processing and image fusion. In the pre-processing, 2D-Adptive filter is applied to enhance the quality of the image. Otsu's segmentation is used to extract tumor region from normal tissues. The segmented region contains skull boundaries in the form of noise; hence morphological operations i.e. erosion and dilation have been applied to remove the extra noise caused by segmentation. Overlay based image fusion is applied to get a clear visual of segmented tumor region. We achieved a detection rate of 93 percent with 7 percent error rate using this dataset. Furthermore, we classify the tumor into benign and malignant based on the size of tumor. Index Terms— MRI imaging, image segmentation, 2D adaptive filter, image fusion.
IJCSIS Papers by Imran Ahmed
Brain tumor segmentation in multimodal magnetic resonance imaging (MRI) images becomes a demandin... more Brain tumor segmentation in multimodal magnetic resonance imaging (MRI) images becomes a demanding task and very much challenging because of the variation in the intensity, location, size and shape of the tumor. In this research work we introduce a simple yet effective method for the detection and segmentation of brain tumor. We proposed dataset of 2000 MRI images, including multiple modalities like T1, T2, FLAIR, Weighted etc. The dataset is divided into two main categories normal brain MRI image and tumor brain MRI image. There are different types of a brain tumor; among the existing, we have considered four most familiar brain tumor types, i.e. CNS Lymphoma, Glioblastoma, Meningioma, and Metastases. The proposed algorithms have four major steps pre-processed MRI image, segmentation, and post-processing and image fusion. The image resizing using bicubic interpolation and a 2D-Adptive filter is applied in the pre-processed stage enhance the image quality for better segmentation the while normalizing the intensity of the MRI image. Otsu segmentation is used to detect tumor region from normal tissues. The Otsu's segmented region contains noisy values like skull boundaries; hence morphological operations i.e. erosion and dilation have been applied to remove the extra noise to get proper tumor region. Overlay based image fusion is applied to get a clear visual of the segmented tumor region. We achieved a detection rate of 94.8 percent with 5.2 percent error rate using this dataset. Furthermore, we manually classify the tumor into their respective category benign and malignant based on the size of the tumor. Index Terms—MRI imaging, tumor types, image segmentation, 2D adaptive filter, image fusion
Uploads
Papers by Imran Ahmed
IJCSIS Papers by Imran Ahmed