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Tumor Detection via Breast Histology Images
Talha Ilyas,Hyongsuk Kim 제어로봇시스템학회 2021 제어로봇시스템학회 각 지부별 자료집 Vol.2021 No.12
Biopsied tissue detection and classification within Breast Histopathology Images is a fundamental prerequisite to estimate the aggressiveness of breast cancer. The development and fully automated pipelines for tissue detection and classification enables the analysis of thousands of tissues within a whole slide histology image, which opens possibilities for analysis and prognosis of breast tumor. There are multiple annotated histology datasets available for evaluating the performance of machine learning models. The number of samples in these datasets is quite limited and usually the annotations provided are in the form of pair of points which points to the center of different types of cells. Most of the works in this field approach this problem by cropping a patch of the WSI usually 50x50 pixels (centered at given point), and then classify these patches with a simple classifier CNN. In this work we propose a method of converting the provided annotations (center points) into bounding box annotations. Then we use Faster-RCNN to detect and classify different types of cells in the WSI in a fully automated pipeline. We also propose data augmentation technique to increase the dataset size for Breast Histology images. Our proposed approach showed an average precision of 70.34% for classification and detection of tumor tissues.
Generic Intent-based Networking Platform for E2E Network Slice Orchestration & Lifecycle Management
Talha Ahmed Khan,Khizar Abbass,Adeel Rafique,Afaq Muhammad,Wang-Cheol Song 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
5G claims the service provisioning for a variety of new applications increasing orchestration complexity. Additionally, many different orchestrators and platforms have been developed over recent years to handle and control the network infrastructures. However, each of the orchestration platforms requires different expertise for handling them. The diversity and dynamicity in terms of requirements made it very complex for the operators to handle runtime operations. As with the increasing number of devices, the amount of traffic streamed over network deviates drastically making it impossible for network operators to handle the network operations manually. Hence automated platforms are a dire need, this work proposed a generic intent-based system that can automatically orchestrate and manage network lifecycle over multiple domains, sites and orchestrators. This system will include the orchestration over multiple domains access, transport and core and it also includes the use of Machine Learning for prediction of resource status for proactive decision making to manage the slice lifecycle automatically.
Fuzzy Logic Based Energy Management For Wind Turbine, Photo Voltaic And Diesel Hybrid System
Talha, Muhammad,Asghar, Furqan,Kim, Sung Ho Korean Institute of Intelligent Systems 2016 한국지능시스템학회논문지 Vol.26 No.5
Rapid population growth with high living standards and high electronics use for personal comfort has raised the electricity demand exponentially. To fulfill this elevated demand, conventional energy sources are shifting towards low production cost and long term usable alternative energy sources. Hybrid renewable energy systems (HRES) are becoming popular as stand-alone power systems for providing electricity in remote areas due to advancement in renewable energy technologies and subsequent rise in prices of petroleum products. Wind and solar power are considered feasible replacement to fossil fuels as the prediction of the fuel shortage in the near future, forced all operators involved in energy production to explore this new and clean source of power. Presented paper proposes fuzzy logic based Energy Management System (EMS) for Wind Turbine (WT), Photo Voltaic (PV) and Diesel Generator (DG) hybrid micro-grid configuration. Battery backup system is introduced for worst environmental conditions or high load demands. Dump load along with dump load controller is implemented for over voltage and over speed protection. Fuzzy logic based supervisory control system performs the power flow control between different scenarios such as battery charging, battery backup, dump load activation and DG backup in most intellectual way.
The effects of flavanoid on the treatment of hepatopulmonary syndrome
Talha Atalay,Murat Cakir,Ahmet Tekin,Tevfik Kucukkartallar,Suleyman Kargin,Adil Kartal,Adnan Kaynak 대한외과학회 2013 Annals of Surgical Treatment and Research(ASRT) Vol.85 No.5
Purpose: Hepatopulmonary syndrome is an arterial oxygenation disorder brought about by advanced liver failure and pulmonary vascular dilatations. The reason why hypoxia develops in hepatopulmonary syndrome depends on the broadening of perialveolar capillary veins. Our study aims to investigate the effects of Flavanoid on hepatopulmonary syndrome through its inhibition of nitric oxide. Methods: Three groups, each having 8 rats, were formed within the scope of our study. Group I (the control group) only received laparatomy, group II received choledoch ligation, and group III was administered Flavanoid (90% flavonoid diosmin, 10% flavonoid hesperidin) following choledoch ligation. The rats were administered Flavanoid at week two following choledoch ligation. The rats’ livers and lungs were examined histopathologically following a five-week follow-up and the perialveolar vein diameters were measured. Arterial blood gases and biochemical parameters were evaluated. Results: It was seen that fibrosis and oxidative damage in the liver with obstructive jaundice as well as hypoxia with pulmonary perialveolar vein sizes were significantly lower than the other group with cirrhosis formed through the administration of Flavanoid. Conclusion: We have concluded that Flavanoid administration might be useful in the treatment of hypoxia in hepatopulmonary syndrome and the delay of cirrhosis contraction.
Talha, Muhammad,Asghar, Furqan,Kim, Sung Ho Korean Institute of Intelligent Systems 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.3
Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.
Talha Usman,Nabiullah Khan,Raghib Nadeem,Abdul Hakim Khan 호남수학회 2019 호남수학학술지 Vol.41 No.1
In the last decades, various integral formulas associated with Bessel functions of different kinds as well as Bessel functions themselves, have been studied and a noteworthy amount of work can be found in the literature. Following up, we present two definite integral formulas involving the product of generalized Bessel function associated with orthogonal polynomials. Also, some intriguing special cases of our main results have been discussed.
Talha Jawaid,Mehnaz Kamal,Richa Singh,Deepa Shukla,Vidya Devanathadesikan,Mukty Sinha 경희대학교 융합한의과학연구소 2018 Oriental Pharmacy and Experimental Medicine Vol.18 No.3
The aim of this study was to investigate the anticonvulsant and neuroprotective eff ects of methanolic extract of Cinnamomum camphora leaves (MECC) in albino wistar rats against maximal electroshock seizure (MES) and seizures induced by pentylenetetrazol (PTZ) models. Acetylcholinesterase (AChE) activity and oxidative stress parameters like malondialdehyde (MDA) and reduced glutathione (GSH) were estimated in the brains after completion of the anticonvulsant studies. MECC (50 and 100 mg/kg b.w., p.o. ) exhibited anticonvulsant activity as indicated by signifi cant ( P < 0.05, P < 0.01) reduction in the duration of hind limb tonic extensor phase in MES induced seizure model and signifi cantly ( P < 0.05, P < 0.01) increased the time of onset of clonic convulsion, decreased the duration of seizures, increased the % protection and decreased the percent mortality in a dose dependent manner. MECC (50 and 100 mg/kg b.w., p.o. ) exhibited neuroprotective activity as indicated by signifi cant reduction in MDA levels, AChE activity and increased GSH level also in a dose dependent manner. These results indicate that MECC may exert anticonvulsant and neuroprotective eff ects which may be attributed to the increase in the level of GABA, inhibition of AChE and infl ammation and antioxidant activity in the brain.
A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics
Talha Ilyas,Hyongsuk Kim 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
In Korea, strawberry producers lack efficient and precise yield forecasts, which would allow them to deploy optimal manpower, equipment, and other resources for harvesting, shipping, and marketing. Reliable estimation of the quantity of strawberry fruit with respect to their ripeness level is critical for forecasting the upcoming strawberry production. Typically, the quantity and ripeness of fruits are estimated manually, which is labor-intensive and time-consuming. In this case, automated yield prediction based on robotic agriculture is a realistic option. We provide in this study an automated strawberry fruit recognition and counting system for accurate and reliable yield prediction. This paper proposes a unique neural network training approach for strawberry fruit counting and ripeness detection that combines semantic graphics for data annotation with a fully convolutional neural network (FCN). Semantic graphics, the suggested data annotation approach, is straightforward to apply, and the desired targets can be quickly tagged with little effort. Moreover, the proposed FCN is an enhanced encoder-decoder network designed specifically for efficient learning of semantic graphics. Quantitative analysis of proposed algorithm showed 4.47% increase in detection accuracy over prior techniques while running at higher frames per second.