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( Adnan Farooq ),( Ahmad Jalal ),( Shaharyar Kamal ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.
A Survey of Human Action Recognition Approaches that use an RGB-D Sensor
Farooq, Adnan,Won, Chee Sun The Institute of Electronics and Information Engin 2015 IEIE Transactions on Smart Processing & Computing Vol.4 No.4
Human action recognition from a video scene has remained a challenging problem in the area of computer vision and pattern recognition. The development of the low-cost RGB depth camera (RGB-D) allows new opportunities to solve the problem of human action recognition. In this paper, we present a comprehensive review of recent approaches to human action recognition based on depth maps, skeleton joints, and other hybrid approaches. In particular, we focus on the advantages and limitations of the existing approaches and on future directions.
Human Action Recognition via Depth Maps Body Parts of Action
( Adnan Farooq ),( Faisal Farooq ),( Anh Vu Le ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5
Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.
Challenges and Issues of Resource Allocation Techniques in Cloud Computing
( Adnan Abid ),( Muhammad Faraz Manzoor ),( Muhammad Shoaib Farooq ),( Uzma Farooq ),( Muzammil Hussain ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.7
In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.
Intelligent Character Recognition System for Account Payable by using SVM and RBF Kernel
Farooq, Muhammad Umer,Kazi, Abdul Karim,Latif, Mustafa,Alauddin, Shoaib,Kisa-e-Zehra, Kisa-e-Zehra,Baig, Mirza Adnan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.11
Intelligent Character Recognition System for Account Payable (ICRS AP) Automation represents the process of capturing text from scanned invoices and extracting the key fields from invoices and storing the captured fields into properly structured document format. ICRS plays a very critical role in invoice data streamlining, we are interested in data like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. As companies attempt to cut costs and upgrade their processes, accounts payable (A/P) is an example of a paper-intensive procedure. Invoice processing is a possible candidate for digitization. Most of the companies dealing with an enormous number of invoices, these manual invoice matching procedures start to show their limitations. Receiving a paper invoice and matching it to a purchase order (PO) and general ledger (GL) code can be difficult for businesses. Lack of automation leads to more serious company issues such as accruals for financial close, excessive labor costs, and a lack of insight into corporate expenditures. The proposed system offers tighter control on their invoice processing to make a better and more appropriate decision. AP automation solutions provide tighter controls, quicker clearances, smart payments, and real-time access to transactional data, allowing financial managers to make better and wiser decisions for the bottom line of their organizations. An Intelligent Character Recognition System for AP Automation is a process of extricating fields like Vendor Name, Purchase Order Number, Due Date, Total Amount, Payee Name, etc. based on their x-axis and y-axis position coordinates.
Adnan Yaqub,Syed Atif Pervez,Umer Farooq,Mohsin Saleem,도칠훈,You-Jin Lee,Minji Hwang,Jeong-Hee Choi,Doohun Kim 한국물리학회 2014 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.65 No.3
A new conductive material, copper/Super-P carbon black composite (Cu-SPB), is prepared viaan efficient ion reducing method for use in low-temperature lithium-ion batteries (LIBs). Thepresent study investigated the effects of copper content on the low-temperature performance ofLIBs. Electrodes prepared with a high-copper-content conductive material (Cu = 18.54%) showedremarkably improved performance in terms of capacity retention (around 40%), cycling stability, andcolumbic efficiency. The electrochemical impedance spectroscopy (EIS) analysis revealed that thepresence of higher Cu contents could reduce the cell’s impedance. The results were also confirmedby using a coin-type full cell’s improved capacity retention, which indicated the significance of Cuparticles in enhancing the low-temperature performance of LIBs.
Electrically exploded silicon/carbon nanocomposite as anode material for lithium-ion batteries.
Farooq, Umer,Choi, Jeong-Hee,Kim, Doohun,Pervez, Syed Atif,Yaqub, Adnan,Hwang, Min-Ji,Lee, You-Jin,Lee, Won-Jae,Choi, Hae-Young,Lee, Sang-Hoon,You, Ji-Hyun,Ha, Chung-Wan,Doh, Chil-Hoon American Scientific Publishers 2014 Journal of Nanoscience and Nanotechnology Vol.14 No.12
<P>In this work, silicon (Si) containing carbon coated core-shell nanostructures were synthesized by electrical explosion of Si wires in ethanol solution followed by high energy mechanical milling (HEMM) process. Material characterization was carried-out using transmission electron microscopy (TEM), field-emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) analysis. HEMM led to very fine and amorphous Si particles in the presence of carbon and inactive Silicon-Carbide (SiC) matrix. These Si based nanocomposites, obtained through electrical explosion followed by HEMM (milled sample), exhibited enhanced electrochemical performance than unmilled nanocomposites, when evaluated as anode material for lithium-ion batteries (LIBs). On completion of (the) 1st cycle, milled and unmilled sample(s) showed specific discharge capacities around 825 mAh/g and 717 mAh/g, respectively. Interestingly, the coulombic efficiencies of milled and unmilled samples were 98.5% and 97% after 60th cycle respectively. The enhanced electrochemical performance is attributed to fine and amorphous Si based nanocomposite obtained through HEMM process.</P>
An Application of Machine Learning in Retail for Demand Forecasting
Muhammad Umer Farooq,Mustafa Latif,Waseem,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.
An Application of Machine Learning in Retail for Demand Forecasting
Muhammad Umer Farooq,Mustafa Latif,Waseemullah,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.