Reactive sintering of lead-free piezoelectric (K0.5Na0.5) NbO3 ceramics

Muhammad Umer FAROOQ,John G. Fisher,김진하,김대웅,신의철,김영훈,김지훈,문수현,이종숙,Xiujuan LIN,Dou Zhang 한양대학교 세라믹연구소 2016 Journal of Ceramic Processing Research Vol.17 No.4

In this paper, (K0.5Na0.5)NbO3 (KNN) ceramics are prepared by a simple and cost- effective reactive sintering method. Startingmaterials of K2CO3, Na2CO3 and Nb2O5 are mixed by ball-milling, pressed into pellets and sintered at 1080 oC, 1100 oC and1120 oC for between 0.25-3 hours. The Archimedes density of samples sintered at 1120 oC reached 91% of the theoreticaldensity after 30 min sintering time. All samples show a single phase perovskite structure after sintering. Samples sintered at1080 oC have a fine-grained microstructure whereas samples sintered at 1100 oC and 1120 oC show abnormal grain growth. Samples sintered at 1120 oC for 1 hour show dielectric, ferroelectric and piezoelectric properties comparable to those ofconventionally-sintered KNN. A conduction mechanism with activation energy of 0.62 eV for the high leakage in the KNNsystem was revealed by impedance spectroscopy.

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.

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.

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.

Detection and Recognition of Vehicle License Plates using Deep Learning in Video Surveillance

Farooq, Muhammad Umer,Ahmed, Saad,Latif, Mustafa,Jawaid, Danish,Khan, Muhammad Zofeen,Khan, Yahya International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.11

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

Implementing Firewall to Mitigate YOYO Attack on Multi Master Cluster Nodes Using Fail2Ban

Muhammad Faraz Hyder,Muhammad Umer Farooq,Mustafa Latif,Faizan Razi Khan,Abdul Hameed,Noor Qayyum Khan,M. Ahsan Siddiqui International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.2

Web technology is evolving with the passage of time, from a single node server to high availability and then in the form of Kubernetes. In recent years, the research community have been trying to provide high availability in the form of multi master cluster with a solid election algorithm. This is helpful in increasing the resources in the form of pods inside the worker node. There are new impact of known DDoS attack, which is utilizing the resources at its peak, known as Yoyo attack. It is kind of burst attack that can utilize CPU and memory to its limit and provide legit visitors with a bad experience. In this research, we tried to mitigate the Yoyo attack by introducing a firewall at load-balancer level to prevent the attack from going to the cluster network.

Impacts of USAID and development assistance toward counterterrorism efforts: Empirical evidence in context of Pakistan

Umer Shahzad,Fengming Qin,Muhammad Umar Farooq 한국사회복지학회 2019 Asian Social Work and Policy Review Vol.13 No.3

This article studies whether the pursuit of foreign aid for counterterrorism purposes militarizes or mitigates terrorism. It focuses on the USAID and official development assistance (ODA) flows to Pakistan, which recently has experienced an increase due to the presence of deadliest terrorist organizations. By using the time series data from 1985 to 2016, the paper investigated the foreign aid and terrorism nexus for pre-9/11 and post-9/11 periods. The empirical estimations of autoregressive distrib-uted lag bound testing approach reported that an increase in military expenditures fuels terrorism in post-9/11 period and the ODA helps to control terrorism from the country. On the contrary, USAID reported insignificant response toward terrorist at-tacks in pre- and post-9/11 periods, suggesting that the foreign aid from the United States has no significant impact on counterterrorism policies for Pakistan. The out-comes of the current study can be utilized in policymaking of counterterrorism and to explore the nexus between foreign aid, terrorism, and military expenditures. The paper concludes that the concerns about the use of foreign aid as counterterrorism tool are warranted, but that actual manifestations are nuanced

High performance lead-free piezoelectric 0.96(K_0.48Na_0.52)NbO₃-0.03[Bi_0.5(Na_0.7K_0.2Li_0.1)_0.5]ZrO₃-0.01(Bi_0.5Na_0.5)TiO₃ sin

Uwiragiye, Eugenie,Farooq, Muhammad Umer,Moon, Su-Hyun,Pham, Thuy Linh,Nguyen, Dang Thanh,Lee, Jong-Sook,Fisher, John G. Elsevier 2017 Journal of the European Ceramic Society Vol.37 No.15

<P><B>Abstract</B></P> <P>0.96(K<SUB>0.48</SUB>Na<SUB>0.52</SUB>)NbO<SUB>3</SUB>-0.03[Bi<SUB>0.5</SUB>(Na<SUB>0.7</SUB>K<SUB>0.2</SUB>Li<SUB>0.1</SUB>)<SUB>0.5</SUB>]ZrO<SUB>3</SUB>-0.01(Bi<SUB>0.5</SUB>Na<SUB>0.5</SUB>)TiO<SUB>3</SUB> single crystals were grown for the first time by the solid state crystal growth method, using [001] or [110]-oriented KTaO<SUB>3</SUB> seed crystals. The grown single crystal shows a dielectric constant of 2720 and polarization-electric field loops of a lossy normal ferroelectric, with P<SUB>r</SUB> =45μC/cm<SUP>2</SUP> and E<SUB>c</SUB> =14.9kV/cm, while the polycrystalline samples with a dielectric constant of 828 were too leaky for P-E measurement due to humidity effects. The single crystal has orthorhombic symmetry at room temperature. Dielectric permittivity peaks at 26°C and 311°C, respectively, are attributed to rhombohedral-orthorhombic and tetragonal–cubic phase transitions. Additionally, Raman scattering shows the presence of an orthorhombic-tetragonal phase transition at ∼150°C, which is not indicated in the permittivity curves but by the loss tangent anomalies. A transition around 700°C in the high temperature dc conductivity is suggested to be a ferroelastic-paraelastic transition.</P>

Analysis of LinkedIn Jobs for Finding High Demand Job Trends Using Text Processing Techniques

Kazi, Abdul Karim,Farooq, Muhammad Umer,Fatima, Zainab,Hina, Saman,Abid, Hasan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

Growth of Oriented Thick Films of BaFe<SUB>12</SUB>O<SUB>19</SUB> by Reactive Diffusion

John G. Fisher,Hung Vu,Muhammad Umer Farooq 한국자기학회 2014 Journal of Magnetics Vol.19 No.4

Single crystal growth of BaFe12O19 by the solid state crystal growth method was attempted. Seed crystals of α-Fe₂O₃ were pressed into pellets of BaFe12O19 + 2 wt% BaCO₃ and heat-treated at temperatures between 1150°C and 1250°C for up to 100 hours. Instead of single crystal growth taking place on the seed crystal, BaO diffused into the seed crystal and reacted with it to form a polycrystalline reaction layer of BaFe12O19. The microstructure, chemical composition and structure of the reaction layer were studied using scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), x-ray Diffraction (XRD) and micro-Raman scattering and confirmed to be that of BaFe12O19. XRD showed that the reaction layer shows a strong degree of orientation in the (h00)/(hk0) planes in the sample sintered at 1200°C. BaFe12O19 layers with a degree of orientation in the (hk0) planes could also be grown by heat-treating an α-Fe₂O₃ seed crystal buried in BaCO₃ powder.