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Classification of Human Protein in Multiple Cells Microscopy Images Using CNN
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作者 Lina Al-joudi muhammad arif 《Computers, Materials & Continua》 SCIE EI 2023年第8期1763-1780,共18页
The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific... The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473. 展开更多
关键词 CNN PROTEIN PReLU SeLU microscopy images subcellular localization multi-cells
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A Flexible Architecture for Cryptographic Applications: ECC and PRESENT
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作者 muhammad Rashid Omar S.Sonbul +3 位作者 muhammad arif Furqan Aziz Qureshi Saud.S.Alotaibi Mohammad H.Sinky 《Computers, Materials & Continua》 SCIE EI 2023年第7期1009-1025,共17页
This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography(ECC)and PRESENT for cryptographic applications.The features of the proposed work are(i)computation of only the point multiplic... This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography(ECC)and PRESENT for cryptographic applications.The features of the proposed work are(i)computation of only the point multiplication operation of ECC over GF(2163)for a 163-bit key generation,(ii)execution of only the variant of an 80-bit PRESENT block cipher for data encryption&decryption and(iii)execution of point multiplication operation(ECC algorithm)along with the data encryption and decryption(PRESENT algorithm).To establish an area overhead for the flexible design,dedicated hardware architectures of ECC and PRESENT are implemented in the first step,and a sum of their hardware area is computed.Then,the implementation of the proposed flexible architecture for ECC and PRESENT algorithms is presented.Implementation results regarding the area,clock cycles,latency,clock frequency,and power after the place-and-route level on Xilinx Virtex-5,Virtex-6,and Virtex-7 FPGA devices are presented.Hence,the implementation results and comparisons show that the proposed architecture suits applications demanding flexible implementation of cryptographic applications. 展开更多
关键词 Flexible UNIFIED design ECC PRESENT FPGA
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A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection
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作者 Shroog Alshomrani muhammad arif Mohammed A.Al Ghamdi 《Computers, Materials & Continua》 SCIE EI 2023年第6期5717-5742,共26页
Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligenc... Coronavirus has infected more than 753 million people,ranging in severity from one person to another,where more than six million infected people died worldwide.Computer-aided diagnostic(CAD)with artificial intelligence(AI)showed outstanding performance in effectively diagnosing this virus in real-time.Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients.This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs.We used the methodology of systematic reviews and meta-analyses(PRISMA)flow method.This research aims to systematically analyze the supervised deep learning methods,open resource datasets,data augmentation methods,and loss functions used for various segment shapes of COVID-19 infection from computerized tomography(CT)chest images.We have selected 56 primary studies relevant to the topic of the paper.We have compared different aspects of the algorithms used to segment infected areas in the CT images.Limitations to deep learning in the segmentation of infected areas still need to be developed to predict smaller regions of infection at the beginning of their appearance. 展开更多
关键词 COVID-19 segmentation chest CT images deep learning systematic review 2D and 3D supervised deep learning
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Introgression of Drought Tolerance into Elite Basmati Rice Variety through Marker-Assisted Backcrossing
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作者 muhammad Sabar Shahzad Amir Naveed +6 位作者 Shahid Masood Shah Abdul Rehman Khan muhammad Musaddiq Shah Tahir Awan muhammad Ramzan Khan Zaheer Abbas muhammad arif 《Phyton-International Journal of Experimental Botany》 SCIE 2023年第5期1421-1438,共18页
Drought is one of the major abiotic threat to rice production in the context of climate change.Super Basmati is an elite,fine grain basmati rice variety grown in Punjab,Pakistan.Due to drought sensitive in nature,its ... Drought is one of the major abiotic threat to rice production in the context of climate change.Super Basmati is an elite,fine grain basmati rice variety grown in Punjab,Pakistan.Due to drought sensitive in nature,its yield has been facing an alarming situation in production because of gradual decrease in irrigated water for a couple of years.Three reported novel QTLs for drought tolerance were selected for incorporation into Super Basmati by employing marker assisted selection strategy.IR55419-04 with novel QTLs was used as a donor parent.Foreground selection was performed by applying PCR based QTL linked SSR markers followed by recombinant selection by using 2-4 flanking markers.Background selection was exercised by using polymorphic SSR markers for maximum genome recovery of the Super Basmati.The individuals homozygous at the target QTLs and with maximum background of Super Basmati at the rest of the non-target genome was selected for evaluation of drought tolerance.Under drought stress conditions,the yields of all introgressed lines(ILs)were 44.2%-125.7%higher than recurrent parent.Six superior ILs that are drought tolerant and very similar to Super Basmati in terms of agronomic and grain quality traits are marked for release as drought-tolerant varieties in arid regions or for use in breeding programs of high grain quality and drought-tolerant parents. 展开更多
关键词 Drought QTL SSR markers basmati rice DROUGHT marker assisted breeding
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Improving Quantitative and Qualitative Characteristics of Wheat (Triticum aestivum L.) through Nitrogen Application under Semiarid Conditions
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作者 muhammad Rafiq muhammad Saqib +12 位作者 Husnain Jawad Talha Javed Sadam Hussain muhammad arif Baber Ali muhammad Sultan Ali Bazmi Ghulam Abbas Marjan Aziz Mohammad Khalid Al-Sadoon Aneela Gulnaz Sobhi F.Lamlom muhammad Azeem Sabir Jameel Akhtar 《Phyton-International Journal of Experimental Botany》 SCIE 2023年第4期1001-1017,共17页
Nitrogen(N),the building block of plant proteins and enzymes,is an essential macronutrient for plant functions.A field experiment was conducted to investigate the impact of different N application rates(28,57,85,114,1... Nitrogen(N),the building block of plant proteins and enzymes,is an essential macronutrient for plant functions.A field experiment was conducted to investigate the impact of different N application rates(28,57,85,114,142,171,and 200 kg ha^(−1))on the performance of spring wheat(cv.Ujala-2016)during the 2017–2018 and 2018–2019 growing seasons.A control without N application was kept for comparison.Two years mean data showed optimum seed yield(5,461.3 kg ha^(−1))for N-application at 142 kg ha^(−1) whereas application of lower and higher rates of N did not result in significant and economically higher seed yield.A higher seed yield was obtained in the 2017–2018(5,595 kg ha^(−1))than in the 2018–2019(5,328 kg ha^(−1))growing seasons under an N application of 142 kg ha^(−1).It was attributed to the greater number of growing degree days in the first(1,942.35°C days)than in the second year(1,813.75°C).Higher rates of N(171 and 200 kg ha^(−1))than 142 kg ha^(−1) produced more number of tillers(i.e.,948,300 and 666,650 ha^(−1),respectively).However,this increase did not contribute in achieving higher yields.Application of 142,171,and 200 kg ha^(−1) resulted in 14.15%,15.0%and 15.35%grain protein concentrations in comparison to 13.15%with the application of 114 kg ha^(−1).It is concluded that the application of N at 142 kg ha^(−1) could be beneficial for attaining higher grain yields and protein concentrations of wheat cultivar Ujala-2016. 展开更多
关键词 Economical yield growing degree days nitrogen Ujala-2016 WHEAT
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A Deep Trash Classification Model on Raspberry Pi 4
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作者 Thien Khai Tran Kha Tu Huynh +2 位作者 Dac-Nhuong Le muhammad arif Hoa Minh Dinh 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2479-2491,共13页
Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloadi... Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloading of garbage dis-posal systems and efficiently promote recycling activities;thus,development of such a classification system is topical and urgent.This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time.An image dataset isfirst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classification module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classification system.This proves the efficiency and high applicability of the proposed system. 展开更多
关键词 Trash classification ResNet raspberry pi internet of things(IoT) deep learning
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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach
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作者 V.Dhilip Kumar J.Praveenchandar +3 位作者 muhammad arif Adrian Brezulianu Oana Geman Atif Ikram 《Computers, Materials & Continua》 SCIE EI 2023年第11期2179-2188,共10页
Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize... Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the Internet.In a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency.It is also known as a server pool or server farm.When a single node is overwhelmed,balancing the workload is needed to manage unpredictable workflows.The load balancer sends the load to another free node in this case.We focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling process.One of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at hand.Depending on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%. 展开更多
关键词 Load balancing throttled algorithm efficient resource allocation
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A Deep Learning Model of Traffic Signs in Panoramic Images Detection
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作者 Kha Tu Huynh Thi Phuong Linh Le +1 位作者 muhammad arif Thien Khai Tran 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期401-418,共18页
To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate go... To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11. 展开更多
关键词 Deep learning convolutional neural network Mask R-CNN traffic signs detection
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巴基斯坦萨戈达地区危害桔园的桃实蝇时空分布(英文)
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作者 muhammad arif muhammad SIDDIQUE AASI +5 位作者 muhammad FAROOQ Habib ALI Saif UL ISLAM muhammad ASAD muhammad SHAKEEL 吴祖建 《昆虫学报》 CAS CSCD 北大核心 2017年第12期1457-1466,共10页
【目的】本研究旨在监测2009-2011年Tehsil Sargodha 7个地点(Sargodha-Ⅰ,Sargodha-Ⅱ,Bhagtanwala,Sakessar,Chak#75-SB,Chak#46-SB和Chak#104-NB)中桃实蝇Bactrocera zonata优势种的种群变化情况。【方法】通过甲基丁香酚信息素诱捕... 【目的】本研究旨在监测2009-2011年Tehsil Sargodha 7个地点(Sargodha-Ⅰ,Sargodha-Ⅱ,Bhagtanwala,Sakessar,Chak#75-SB,Chak#46-SB和Chak#104-NB)中桃实蝇Bactrocera zonata优势种的种群变化情况。【方法】通过甲基丁香酚信息素诱捕器诱捕桃实蝇,每周统计记录桃实蝇种群发生情况,诱捕器每两周加药一次。【结果】结果表明,在调查的3年(2009-2011)中,Sargodha-Ⅰ的桃实蝇种群多度最高(分别为53.67,45.82和45.47头/诱捕器),其次为Sakessar(分别为41.13,33.87和35.75头/诱捕器),而Chak#75-SB的种群多度最低(分别为15.78,19.18和19.15头/诱捕器)。每年桃实蝇发生最高峰出现在4月(分别为76.08,71.94和61.51头/诱捕器),其次为5月(分别为60.74,52.63和64.00头/诱捕器),而在2月和10月发生量最低。另外,桃实蝇种群多度与最高和最低气温呈较强的正相关,而与相对湿度和降雨量呈负相关。同样,回归系数表明,最高气温是影响桃实蝇种群发生的主要因素,而降雨量的影响最小。【结论】应当全年对桃实蝇进行定期监测,由于气象因子严重影响桃实蝇的种群发生情况,因此尤其应监测其在4-5月温度开始上升时的发生情况。 展开更多
关键词 实蝇科 桃实蝇 种群动态 气象因子 柑橘 信息素诱捕器
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An Investigation into Infection Prevention and Control Practices among Close Contacts of COVID-19 Positive Cases Identified during Trace Test and Quarantine Activities at District Quetta (Unmatched Case-Control Study)
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作者 muhammad arif Abid Saeed +10 位作者 muhammad Abdullah Ambreen Chaudhary Zakir Hussain Mirza Zeeshan Iqbal Baig Zubair Ahmed Khoso Mir Abdul Qadir Sheikh Ahmed Saher Sultan Zahra Gauhar Ayesha Babar Kawish Ehsan Larik 《Open Journal of Epidemiology》 2021年第4期360-370,共11页
The second wave of COVID-19 pandemic has started globally, right now 220 countries are infected and a total of 71,351,695 confirmed cases and 1,612,372 deaths due to COVID-19 have been reported. Infection Prevention a... The second wave of COVID-19 pandemic has started globally, right now 220 countries are infected and a total of 71,351,695 confirmed cases and 1,612,372 deaths due to COVID-19 have been reported. Infection Prevention and Control (IPC) measures for COVID-19 all have proved vital in decreasing the transmission rates among the communities. <strong>Methodology:</strong> Unmatched Case-Control Study was conducted where cases were defined as “every PCR positive contact (symptomatic or asymptomatic) for any index case” similarly controls were defined as “every PCR negative contact (symptomatic or asymptomatic) for any index case who was home quarantined for 14 days based on suspicion by PDSRU team”. A simple random technique was used and 300 individuals were made part of this study. <strong>Results:</strong> The major findings of this study shows that PCR positive contacts poorly adopted certain COVID-19 IPC measures of interest in their daily life hence got infected. The odds for all the variables of interest were found to be statistically significant among cases as compared to controls like the odds for knowingly and intentionally contacted with a COVID-19 positive case was 13.7 times more among the PCR positive contacts as compare to PCR negative contacts (p = 0.00, C.I = 7.62 - 24.90), similarly, the odds of being a family member of the index COVID-19 case was 7.07 times more among the PCR positive contacts as compared to the PCR negative contacts (p = 0.00, C.I = 3.25 - 15.86). <strong>Conclusion:</strong> Before the development and availability of a vaccine, the only tools that can help prevent the spread of COVID-19 are IPC measures. 展开更多
关键词 COVID-19 IPC Measures Contact PCR PDSRU
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Risk of “On Job Non Compliance” towards Various COVID-19 Standard &Transmission Based Infection Prevention &Control Measures/Precautions among the Healthcare Workers Working in OPD Settings of Public Sector Tertiary Care Hospitals of Quetta Balochistan (Prospective Cohort Study)
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作者 muhammad arif muhammad Abdullah +10 位作者 Abid Saeed Ambreen Chaudhary Zakir Hussain Ayesha Babar Kawish Mir Abdul Qadir Sheikh Ahmed Saher Sultan Zubair Ahmed Khoso Zahra Gauhar Tahira Kamal Ehsan Larik 《Open Journal of Epidemiology》 2021年第4期390-401,共12页
<strong>Background:</strong> COVID-19 Pandemic is still circulating within the human population and proving to be a deadlier disease with a mortality rate ranging from 0.5% to 7%. Since COVID-19 is a highl... <strong>Background:</strong> COVID-19 Pandemic is still circulating within the human population and proving to be a deadlier disease with a mortality rate ranging from 0.5% to 7%. Since COVID-19 is a highly transmissible disease;there is always a probability for its outward spread towards the general public and community from the hospitals and healthcare facilities where they come to seek treatment. <strong>Methodology:</strong> A prospective cohort study design was used, considering the limited available resources and time—a total of 200 healthcare workers (including doctors, nurses, para-medical staff, janitorial staff, reception staff & pharmacists) working in the OPDs of the two major public sector hospitals of Quetta were made part of this study. The study participants were selected using a simple random sampling technique and selection was made from the daily attendance register. The study participants from “Hospital-A” were first of all educated and trained on various COVID-19 IPC measures later on various COVID-19-IEC materials;written in simple Urdu language, were displayed clearly everywhere in the OPD. Similarly, handwashing stations along with hand sanitizers/soaps and surgical face masks were also made available free of cost for all the study participants of Hospital-A. Moreover the importance and effectiveness of COVID-19 IPC measures were continuously announced in the OPD gallery of Hospital-A, these announcements used simple wording in local languages (<em>i.e.</em>, Urdu, Pashto, Balochi and Brahvi). On the other hand, in the OPD of “Hospital-B”, no such interventions were made. The study participants of both the hospitals were followed for one month and observations like which group showed more on-job noncompliance towards various COVID-19 IPC measures were recorded. The data was recorded on daily basis (from 1<sup>st</sup> May-to-31<sup>st</sup> May 2021) after observing the study participants for compliance towards using face masks, face shields, personal protective gowns, gloves, hand sanitizers, maintaining 6 feet social distancing and implanting triage at his or her OPD counter. Any study participant with daily proper practice of at least face masks, gloves, hand sanitizer and maintaining a 6 feet social distancing SOPs during duty hours at the outdoor patients department was considered to be a compliant individual if even one of these minimum required SOPs has not practiced the study participant, he/she was classified as non-compliant individual. A checklist was used to record these findings for every study participant on daily basis by trained data collectors. Lastly, all the data was analyzed using Microsoft Excel 2007 version. <strong>Results:</strong> The major findings of this study are almost in line with the set objectives, the study results are clearly showing the Risk Ratio (RR) as 0.27, indicating that the intervention group participants were only 27% as likely to develop on-job non-compliance for various COVID-19 IPC measures compare to the non-intervention group. <strong>Discussion & Conclusion:</strong> It is highly recommended that various COVID-19 specific infection prevention and control interventions like COVID-19 IPC trainings, COVID-19 IEC and BCC materials be displayed clearly everywhere in the healthcare facilities especially in the OPD department. Moreover, audio announcements made in simple wording using local languages like Urdu, Pashto, Balochi and Brahvi could really serve as constant reminder tools especially in an OPD department where every next patient in the queue could present with a different infectious bug. 展开更多
关键词 COVID-19 IPC Measures IEC & BCC Materials
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COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries 被引量:5
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作者 Saleh Albahli Ahmad Algsham +5 位作者 Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed Hafiz Tayyab Rauf muhammad arif Mazin Abed Mohammed 《Computers, Materials & Continua》 SCIE EI 2021年第5期1613-1627,共15页
Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brough... Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms.This phenomenon caused a state of panic among people.Different studies were conducted to stop the spread of fake news to help people cope with the situation.In this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)techniques.It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative reactions.The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment overall.The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly. 展开更多
关键词 COVID-19 sentiment analysis natural language processing TWITTER social data mining sentiment polarity
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Evaluation of Humic Acid Application Methods for Yield and Yield Components of Mungbean 被引量:8
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作者 muhammad Waqas Bashir Ahmad +6 位作者 muhammad arif Fazal Munsif Abdul Latif Khan muhammad Amin Sang-Mo Kang Yoon-Ha Kim In-Jung Lee 《American Journal of Plant Sciences》 2014年第15期2269-2276,共8页
A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawa... A triplicate field experiment laid out in randomized complete block design was conducted to evaluate different humic acid (HA) application methods at Agricultural Research Farm, of KPK Agricultural University, Peshawar. Three methods of HA application: seed priming, foliar spray and soil application were included in the experiment. Humic acid application methods significantly affected pods plant-1, grains pod-1, 1000 grain weights, and grain yield whereas biological yield was not significantly affected by HA application methods. Humic acid application at the rate of 3 kg&middotha-1 resulted in higher number of pods plant-1, thousand grain weights and grain yield, however it was statistically similar to the treatments where HA was soil applied at rate of 1 and 2 kg&middotha-1, seed priming with 0% (water soaked), 1%, 2% HA solution and foliar spray with 0.01%, 0.05% and 0.1% of HA solution. It is concluded that HA application in all the three methods significantly enhances grain yield and yield components of mungbean. 展开更多
关键词 MUNGBEAN HUMIC Acid SEED PRIMING FOLIAR Spray Soil Application Yield
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Highly active sites of NiVB nanoparticles dispersed onto graphene nanosheets towards efficient and pH-universal overall water splitting 被引量:4
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作者 muhammad arif Ghulam Yasin +5 位作者 muhammad Shakeel muhammad Asim Mushtaq Wen Ye Xiaoyu Fang Shengfu Ji Dongpeng Yan 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2021年第7期237-246,共10页
Production of hydrogen(H2) and oxygen(O2) through electrocatalytic water splitting is one of the sustainable,green and pivotal ways to accomplish the ever-increasing demands for renewable energy sources,but remains a ... Production of hydrogen(H2) and oxygen(O2) through electrocatalytic water splitting is one of the sustainable,green and pivotal ways to accomplish the ever-increasing demands for renewable energy sources,but remains a big challenge because of the uphill reaction during overall water splitting.Herein,we develop high-performance non-noble metal electrocatalysts for pH-universal water splitting,based on nickel/vanadium boride(NiVB) nanoparticles/reduced graphene oxide(rGO) hybrid(NiVB/rGO)through a facile chemical reduction approach under ambient condition.By virtue of more exposure to surface active sites,superior electron transfer capability and strong electronic coupling,the asprepared NiVB/rGO heterostructure needs pretty low overpotentials of 267 and 151 mV to deliver a current density of 10 mA cm^(-2) for oxygen evolution reaction(OER) and hydrogen evolution reaction(HER)respectively,with the corresponding Tafel slope of 44 and 88 mV dec^(-1) in 1.0 M KOH.Moreover,the NiVB/rGO electrocatalysts display a promising performance in a wide-pH conditions that require low overpotential of 310,353 and 489 mV to drive a current density of 10 mA cm^(-2) for OER under 0.5 M KOH,0.05 M H2SO4 and 1.0 M phosphate buffer solution(PBS) respectively,confirming the excellent electrocata lytic performance among state-of-the-art Ni-based electrocatalysts for overall water splitting.Therefore,the interfacial tuning based on incorporation of active heterostructure may pave a new route to develop bifunctional,cost-effective and efficient electrocatalyst systems for water splitting and H2 production. 展开更多
关键词 ELECTROCATALYSIS Oxygen evolution reaction Hydrogen evolution reaction NiVB/rGO heterostructure pH-universal
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Predicting the Type of Crime: Intelligence Gathering and Crime Analysis 被引量:3
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作者 Saleh Albahli Anadil Alsaqabi +3 位作者 Fatimah Aldhubayi Hafiz Tayyab Rauf muhammad arif Mazin Abed Mohammed 《Computers, Materials & Continua》 SCIE EI 2021年第3期2317-2341,共25页
Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its i... Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%. 展开更多
关键词 PREDICTION machine learning crime prevention naïve bayes crime prediction classification algorithms
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On Computing the Suitability of Non-Human Resources for Business Process Analysis 被引量:2
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作者 Abid Sohail Khurram Shahzad +3 位作者 P.D.D.Dominic muhammad arif Butt muhammad arif muhammad Imran Tariq 《Computers, Materials & Continua》 SCIE EI 2021年第4期303-319,共17页
Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process an... Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method. 展开更多
关键词 Business process management business process improvement process warehouse data warehouse resource suitability component resource suitability health care artificial intelligence
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Rice molecular markers and genetic mapping:Current status and prospects 被引量:4
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作者 Ghulam Shabir Kashif Aslam +8 位作者 Abdul Rehman Khan muhammad Shahid Hamid Manzoor Sibgha Noreen Mueen Alam Khan muhammad Baber muhammad Sabar Shahid Masood Shah muhammad arif 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第9期1879-1891,共13页
Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance pr... Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus(QTL) mapping, and marker-assisted selection(MAS) are evolving into more efficient concepts of linkage disequilibrium(LD) also called association mapping and genomic selection(GS), respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice. 展开更多
关键词 genetic mapping molecular markers maker assisted selection Oryza sativa L quantitative trait loci
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Epidemiologic Evolution Platform Using Integrated Modeling and Geographic Information System 被引量:1
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作者 Adrian Brezulianu Oana Geman +3 位作者 muhammad arif Iuliana Chiuchisan Octavian Postolache Guojun Wang 《Computers, Materials & Continua》 SCIE EI 2021年第5期1645-1663,共19页
At the international level, a major effort is being made to optimizethe flow of data and information for health systems management. The studiesshow that medical and economic efficiency is strongly influenced by the le... At the international level, a major effort is being made to optimizethe flow of data and information for health systems management. The studiesshow that medical and economic efficiency is strongly influenced by the levelof development and complexity of implementing an integrated system of epidemiological monitoring and modeling. The solution proposed and describedin this paper is addressed to all public and private institutions involved inthe fight against the COVID-19 pandemic, using recognized methods andstandards in this field. The Green-Epidemio is a platform adaptable to thespecific features of any public institution for disease management, based onopen-source software, allowing the adaptation, customization, and furtherdevelopment of “open-source” applications, according to the specificities ofthe public institution, the changes in the economic and social environment andits legal framework. The platform has a mathematical model for the spreadof COVID-19 infection depending on the location of the outbreaks so thatthe allocation of resources and the geographical limitation of certain areascan be parameterized according to the number and location of the real-timeidentified outbreaks. The social impact of the proposed solution is due to theplanned applications of information flow management, which is a first stepin improving significantly the response time and efficiency of people-operatedresponse services. Moreover, institutional interoperability influences strategicsocietal factors. 展开更多
关键词 Epidemiology evolution service-oriented architecture PANDEMIC geographic information system epidemiological modeling
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Identification of Thoracic Diseases by Exploiting Deep Neural Networks 被引量:1
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作者 Saleh Albahli Hafiz Tayyab Rauf +2 位作者 muhammad arif Md Tabrez Nafis Abdulelah Algosaibi 《Computers, Materials & Continua》 SCIE EI 2021年第3期3139-3149,共11页
With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may f... With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable consequences.There are only several studies that targeted X-ray images of cardiothoracic diseases.Most of the studies only targeted a single disease,which is inadequate.Although some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data issues.This research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable results.The models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%. 展开更多
关键词 GAN CNN chest diseases inception-V3 ResNet152
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