A Two-Photon Probe for Near-Membrane Zinc Ions

Rathore, Kailash,Lim, Chang Su,Lee, Young,Park, Hee Jung,Cho, Bong Rae Wiley Blackwell (John Wiley Sons) 2014 ASIAN JOURNAL OF ORGANIC CHEMISTRY Vol.3 No.10

Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs

Rathore, M. M.,Son, H.,Ahmad, A.,Paul, A. Springer Science + Business Media 2018 Soft computing Vol.22 No.5

<P>Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary.</P>

Is the Tumor Infiltrating Natural Killer Cell (NK-TILs) Count in Infiltrating Ductal Carcinoma of Breast Prognostically Significant?

Rathore, Ankita Singh,Goel, Madhu Mati,Makker, Annu,Kumar, Sandeep,Srivastava, Anand Narain Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.8

Purpose: The aim of this study was to investigate the prognostic significance of the CD56+NK-TIL count in infiltrating ductal carcinoma (IDC) of breast. Material and Methods: Immunohistochemistry (IHC) was performed using antibodies specific for CD56 on formalin-fixed and paraffin-embedded tissue sections of 175 infiltrating ductal carcinomas (IDC) of breast. Distribution of intratumoral and stromal CD56+NK-TILs was assessed semi-quantitatively. Results: A low intratumoral CD56+count showed significant and inverse associations with tumor grade, stage, and lymph node status, whereas it had significant and direct association with response to treatment indicating good prognosis. These patients had better survival (${\chi}^2$=4.80, p<0.05) and 0.52 fold lower death rate (HR=0.52, 95% CI=0.28-0.93) as compared to patients with high CD56+ intratumoral count. The association of survival was insignificant with low CD56 stromal count as compared to high CD56 stromal count (${\chi}^2$=1.60, p>0.05). Conclusion: To conclude, although NK-TIL count appeared as a significant predictor of prognosis, it alone may not be sufficient for predicting the outcome considering the fact that there exists a crosstalk between NK-TILs and the other immune infiltrating TILs.

A Two-Stage Deep-Learning Model for Detection and Occlusion-Based Classification of Kashmiri Orchard Apples for Robotic Harvesting

Rathore Divya,Divyanth L. G.,Reddy Kaamala Lalith Sai,Chawla Yogesh,Buragohain Mridula,Soni Peeyush,Machavaram Rajendra,Hussain Syed Zameer,Ray Hena,Ghosh Alokesh 한국농업기계학회 2023 바이오시스템공학 Vol.48 No.2

Purpose The process of robotic harvesting has revolutionized the agricultural industry, allowing for more effi cient and costeff ective fruit picking. Developing algorithms for accurate fruit detection is essential for vision-based robotic harvesting of apples. Although deep-learning techniques are popularly used for apple detection, the development of robust models that can accord information about the fruit’s occlusion condition is important to plan a suitable strategy for end-eff ector manipulation. Apples on the tree experience occlusions due to leaves, stems (branches), trellis wire, or other fruits during robotic harvesting. Methods A novel two-stage deep-learning-based approach is proposed and successfully demonstrated for detecting ontree apples and identifying their occlusion condition. In the fi rst stage, the system employs a cutting-edge YOLOv7 model, meticulously trained on a custom Kashmiri apple orchard image dataset. The second stage of the approach utilize the powerful Effi cientNet-B0 model; the system is able to classify the apples into four distinct categories based on their occlusion condition, namely, non-occluded, leaf-occluded, stem/wire-occluded, and apple-occluded apples. Results The YOLOv7 model achieved an average precision of 0.902 and an F1-score of 0.905 on a test set for detecting apples. The size of the trained weights and detection speed were observed to be 284 MB and 0.128 s per image. The classifi cation model produced an overall accuracy of 92.22% with F1-scores of 94.64%, 90.91%, 86.87%, and 90.25% for nonoccluded, leaf-occluded, stem/wire-occluded, and apple-occluded apple classes, respectively. Conclusion This study proposes a novel two-stage model for the simultaneous detection of on-tree apples and classify them based on occlusion conditions, which could improve the eff ectiveness of autonomous apple harvesting and avoid potential damage to the end-eff ector due to the objects causing the occlusion.

Real-time secure communication for Smart City in high-speed Big Data environment

Rathore, M. Mazhar,Paul, Anand,Ahmad, Awais,Chilamkurti, Naveen,Hong, Won-Hwa,Seo, HyunCheol Elsevier 2018 Future generation computer systems Vol.83 No.-

<P><B>Abstract</B></P> <P>The recent development in the technology brings the concept of Smart City that is achieved through real-time city related intelligent decisions by analyzing the data harvested from various smart systems in the city using millions of sensors and devices connected over the Internet, termed as Internet of Things (IoT). These devices generate the overwhelming volume of high-speed streaming data, termed as Big Data. However, the generation of city data at a remote location and then transmitting it to central city servers for analysis purpose raises the concerns of security and privacy. On the other hand, providing security to such Big Data streaming requires a high-speed security system that can work in a real-time environment without providing any delay that may slow down the overall performance of the Smart City System. To overthrown these challenges, in this paper, we proposed an efficient and real-time Smart City security system by providing strong intrusion detection at intelligent city building (ICB) and also a security protocol to protect the communication between the remote smart system(RSS)/User and the city analysis building, i.e., ICB. The proposed communication security protocol consists of various phases, i.e., registration phase, session key exchange phase, session key revocation phase, and data transmission phases from RSS to ICB as well as from User to ICB. Vast security analyses are performed to evaluate the credibility of the system. The proposed system is also evaluated on efficiency in terms of computation cost and throughput of overall functions used in the system. The system’s evaluation and the comparative study with existing system show that the prosed system is secure, more efficient, and able to work in a real-time, high-speed Smart City environment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> This paper presents a system architecture that integrate Smart City with technical network. </LI> <LI> A Novel notion of Smart-City Network is defined by Communication Security. </LI> <LI> Intelligent Smart Building Architecture with Remote Smart System. </LI> <LI> The system is also implemented of Smart City Decision are done on top of Hadoop parallel nodes. </LI> </UL> </P>

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health

Rathore, M. Mazhar,Ahmad, Awais,Paul, Anand,Wan, Jiafu,Zhang, Daqiang Springer-Verlag 2016 Journal of Medical Systems Vol.40 No.12

Dual-color imaging of cytosolic and mitochondrial zinc ions in live tissues with two-photon fluorescent probes

Rathore, Kailash,Lim, Chang Su,Lee, Young,Cho, Bong Rae The Royal Society of Chemistry 2014 Organic & Biomolecular Chemistry Vol.12 No.21

<P>We report two-photon probes for Zn<SUP>2+</SUP> ions that can simultaneously detect cytosolic and mitochondrial Zn<SUP>2+</SUP> ions in live cells and living tissues at 115 mm depth by dual-color TPM imaging with minimum interference from other biologically relevant species.</P> <P>Graphic Abstract</P><P>We have developed TP probes for [Zn<SUP>2+</SUP>]<SUB>cyto</SUB> and [Zn<SUP>2+</SUP>]<SUB>mito</SUB>, which emit TPEF at widely-separated wavelength regions. The new probes can simultaneously detect [Zn<SUP>2+</SUP>]<SUB>cyto</SUB> and [Zn<SUP>2+</SUP>]<SUB>mito</SUB> in live cells, as well as in living tissues by dual-color TPM imaging. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c4ob00101j'> </P>

Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data

Rathore, M. Mazhar,Paul, Anand,Hong, Won-Hwa,Seo, HyunCheol,Awan, Imtiaz,Saeed, Sharjil Elsevier 2018 Sustainable cities and society Vol.40 No.-

<P><B>Abstract</B></P> <P>Integration of all smart systems (such as smart home, smart parking, etc.) and the IoT devices (such as sensors, actuators, and smartphones) in the city can play a vital role to develop the urban services by building their city digital and smarter. However, interconnection of lots of IoT objects to collect urban data over the Internet to launch a smart digital city, effects vast volume of data generation, termed as Big Data. Thus, it is a challenging task to integrate IoT devices and smart systems in order to harvest and process such big amount of real-time city data in an effective manner aimed at creating a Smart Digital City. Therefore, in this paper, we have established an IoT-based Smart City by using Big Data analytics while harvesting real-time data from the city. We used sensors’ deployment including sensors at smart home, smart parking, vehicular networking, surveillance, weather and water monitoring system, etc., for real time data collection. The complete system is described by its proposed architecture and implementation prototype using Hadoop ecosystem in a real environment. In addition, the Smart Digital City services are extended by developing the intelligent Smart Transportation System by means of big graph processing to facilitate citizens while providing real-time traffic information and alerts. The proposed system consists of number of stages including data generation and collection, aggregation, filtration, classification, preprocessing, computing, and decision making. The efficiency of the system is extended by applying Big Data processing using Apache Spark over Hadoop. Whereas, the big city graph processing is achieved by using Giraph over Hadoop. The system is practically implemented by taken existing smart systems and IoT devices as city data sources to develop the Smart Digital City. The proposed system is evaluated with respect to efficiency in terms of scalability and real-time data processing.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Data Generation, Collection, Aggregation, Filtration, Classification, Preprocessing Computing and Decision Making. </LI> <LI> Impementation of Smart IoT based Digital City using Real-Time Urban Data. </LI> <LI> Big Data Analytics for City Planning using Hadoop Ecosystem. </LI> <LI> Big Graph Processing for Traffic Information and Alert. </LI> <LI> System is Evaluated for its Scalability and Real-time Data Processing. </LI> </UL> </P>

XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs

Rathore, Shailendra,Sharma, Pradip Kumar,Park, Jong Hyuk Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

Distal Aortic Remodeling after Type A Dissection Repair: An Ongoing Mirage

Rathore Kaushalendra Singh 대한흉부외과학회 2021 Journal of Chest Surgery (J Chest Surg) Vol.54 No.6

Remodeling is a commonly encountered term in the field of cardiothoracic surgery that is often used to describe various pathophysiological changes in the dimension, structure, and function of various cardiac chambers, including the aorta. Stanford type A or DeBakey type 1 aortic dissection (TAAD) is a perplexing pathologic condition that can present sur- gical teams with the need to navigate a maze of complex decision-making. Ascending or hemi-arch replacement leaves behind a significant amount of distal diseased aortic tissue, which might have a persistent false lumen or primary or secondary intimal tears (or com- munications between lumina), which can lead to dilatation of the aortic arch. Unfavorable aortic remodeling is a major cause of distal aortic deterioration after the index surgery. Cardiac surgeons are aware of post-surgical cardiac chamber remodeling, but the concept of distal aortic remodeling is still idealized. The contemporary literature from established aortic centers supports aggressive management of the residual aortic pathology during the index surgery, and with continuing technical advancements, endovascular stenting options are readily available for patients with TAAD or for complicated type B aortic dissec- tion cases. This review discusses the pathophysiology and treatment options for favorable distal aortic remodeling, as well as its impact on mid- to long-term outcomes following TAAD repair.