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| 1 | Adsorptive removal of phosphate by the bimetallic hydroxide nanocomposites embedded in pomegranate peel显示文摘This study aimed to fabricate new and effective material for the efficiency of phosphate adsorption.Two types of adsorbent materials,the zirconium hydroxides embedded in pomegranate peel(Zr/Peel)and zirconium-lanthanum hydroxides embedded in pomegranate peel(Zr-La/Peel)were developed.Scanning electronic microscopy(SEM),x-ray photoelectron spectroscopy(XPS)and x-ray diffraction(XRD)were evaluated to give insight into the physicochemical properties of these adsorbents.Zr-La/Peel exceeded the adsorption efficiency of Zr/Peel adsorbents in batch adsorption experiments at the same pH level.The peel as a host can strive to have a strong'shielding effect'to increase the steadiness of the entrenched Zr and La elements.La and Zr are hydroxide metals that emit many hydrogen ions during the hydrolysis reaction,which contribute to protonation and electrostatic attraction.The highest adsorption capacity of La-Zr/Peel for phosphate was calculated to be40.21 mg/g,and pseudo second-order equation is very well fitted for kinetic adsorption.Phosphate adsorption efficiency was reduced by an increase of pH.With the background of coexisting Cl-,little effect on adsorption efficiency was observed,while adsorption capacities were reduced by almost 20-30%with the coexistence of SO42-,NO3-and humic acid(HA). | Muhammad Akram Xing Xu Baoyu Gao Qinyan Yue Shang Yanan Rizwan Khan Muhammad Ali Inam | 2020 | Journal of Environmental Sciences2020，32，5： | 8 |
| 2 | Breeding for pre-harvest sprouting resistance in bread wheat under rainfed conditions显示文摘Pre-harvest sprouting in wheat is the germination of seeds within the spikes when rains occur after or during grain ripening, which occurs commonly in the barani tract of Pakistan. Therefore, 10 cultivars and five advanced lines of spring bread wheat were evaluated for pre-harvest sprouting resistance. After natural rainfall,seeds were immediately collected from the wet spikes and tested for germinating ability. Three different germination tests were applied to hand-threshed seed:(1) spikes threshed on the day of sampling and germination tested immediately,(2) spikes threshed on the day of sampling and germination tested 1 week later, and(3) spikes threshed 1 week after sampling and germination test immediately after threshing. Seeds and spikes kept for 1 week were place on blotting paper at room temperature.Cultivars BARS-09, 09 FJ17, Doukkala-12, NARC-09 and Ouassou-20 exhibited higher sprouting resistance while other genotypes were susceptible to pre-harvest sprouting in each of the three tests. A diallel crossing was conducted with six susceptible and two resistant genotypes to assess the genetic behavior of pre-harvest sprouting resistance.The combining ability(CA) demonstrated a higher proportion of additive genetic effects for sprouting resistance, because of higher variance of general and specific CA for both parameters under study. Doukkala-12 and BARS-09 showed increased pre-harvest sprouting resistance in their F1 descendants. | Muhammad ZEESHAN Waheed ARSHAD Muhammad Imran KHAN Shiraz ALI Ali NAWAZ Amina BATOOL Muhammad TARIQ Muhammad Imran AKRAM Muhammad Amjad ALI | 2018 | Frontiers of Agricultural Science and Engineering2018，5，2： | 1 |
| 3 | Validity of the patient health questionnaires(phq-2 and phq-9)for screening depression among human immunodeficiency virus patients in Lahore,Pakistan显示文摘BACKGROUND Many human immunodeficiency virus(HIV)infected patients suffer from depression,but a little focus is given to detecting and treating depression in primary health care.Detection of depression can be improved by introducing short,reliable,and valid screening instruments.AIM To determine the psychometric properties of the patient health questionnaire-2(PHQ-2)and patient health questionnaire-9(PHQ-9)for depression screening and diagnosis,and the sensitivity and specificity of the PHQ-2 in HIV infected patients.METHODS A cross-sectional study was conducted on 158 HIV-infected patients aged 18 years and above in Lahore,Pakistan.PHQ-2 was implemented to screen depression.PHQ-9 was implemented to diagnose major depressive disorder as a reference standard.Reliability,Validity tests and receiver operating characteristic curve were computed.RESULTS The Cronbach's alpha of PHQ-2 and PHQ-9 were 0.732 and 0.759,respectively.The study results showed that the score of 2 on PHQ-2 indicates the highest Youden's index of 0.924,with both sensitivity and specificity of 0.96,and the area under the curve for PHQ-2 was 0.98(95%CI:0.953-0.998).CONCLUSION Good psychometric properties for the PHQ-2 and PHQ-9 indicated their significant potential as tools for depression screening and diagnosis in the HIV-infected population. | Khunsa Junaid Iqra Akram Muhammad Daood Amjad Khan | 2022 | World Journal of Virology2022，11，5： | 1 |
| 4 | Green Synthesis of Silver Nanoparticles: Structural Features and In Vivo and In Vitro Therapeutic Effects against Helicobacter pylori Induced Gastritis显示文摘 | Muhammad Amin Sadaf Hameed Asghar Ali Farooq Anwar Shaukat Ali Shahid Imran Shakir Aqdas Yaqoob Sara Hasan Safyan Akram Khan Sajjad-ur-Rahman Imre Sovago | 2014 | Bioinorganic Chemistry and Applications2014，，： | 1 |
| 5 | Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach显示文摘The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO client.Although a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development only.Several frameworks have been developed focusing on guiding software systemmanagers concerning offshore software outsourcing.However,none of these studies delivered comprehensive guidelines for managing the whole process of OSMO.There is a considerable lack of research working on managing OSMO from a vendor’s perspective.Therefore,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective.This study validated the preliminary OSMO process model via a case study research approach.The results showed that the OSMO process model is applicable in an industrial setting with few changes.The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model.The refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk. | Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 6 | Video Analytics Framework for Human Action Recognition显示文摘Human action recognition(HAR)is an essential but challenging task for observing human movements.This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms.This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation,features reduction and selection framework.A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted.An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion.A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown features.The features are exploited by a multi-class SVM for action identification.Comprehensive experimental results are undertaken on four action datasets,namely,Weizmann,KTH,Muhavi,and WVU multi-view.We achieved the recognition rate of 96.80%,100%,100%,and 100%respectively.Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches. | Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Fayadh Alenezi Usman Tariq Yunyoung Nam Tallha Akram | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 7 | Feasibility and physics potential of detecting ^(8)B solar neutrinos at JUNO显示文摘The Jiangmen Underground Neutrino Observatory(JUNO)features a 20 kt multi-purpose underground liquid scintillator sphere as its main detector.Some of JUNO's features make it an excellent location for^8B solar neutrino measurements,such as its low-energy threshold,high energy resolution compared with water Cherenkov detectors,and much larger target mass compared with previous liquid scintillator detectors.In this paper,we present a comprehensive assessment of JUNO's potential for detecting^8B solar neutrinos via the neutrino-electron elastic scattering process.A reduced 2 MeV threshold for the recoil electron energy is found to be achievable,assuming that the intrinsic radioactive background^(238)U and^(232)Th in the liquid scintillator can be controlled to 10^(-17)g/g.With ten years of data acquisition,approximately 60,000 signal and 30,000 background events are expected.This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter,which will shed new light on the inconsistency between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework.IfDelta m^(2)_(21)=4.8times10^(-5);(7.5times10^(-5))eV^(2),JUNO can provide evidence of neutrino oscillation in the Earth at approximately the 3sigma(2sigma)level by measuring the non-zero signal rate variation with respect to the solar zenith angle.Moreover,JUNO can simultaneously measureDelta m^2_(21)using^8B solar neutrinos to a precision of 20% or better,depending on the central value,and to sub-percent precision using reactor antineutrinos.A comparison of these two measurements from the same detector will help understand the current mild inconsistency between the value of Delta m^2_(21)reported by solar neutrino experiments and the KamLAND experiment. | Angel Abusleme Thomas Adam Shakeel Ahmad Sebastiano Aiello Muhammad Akram Nawab Ali Fengpeng An Guangpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Andrej Babic Wander Baldini Andrea Barresi Eric Baussan Marco Bellato Antonio Bergnoli Enrico Bernieri David Biare Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Max Buesken Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Jinfan Chang Yun Chang Pingping Chen Po-An Chen Shaomin Chen Shenjian Chen Xurong Chen Yi-Wen Chen Yixue Chen Yu Chen Zhang Chen Jie Cheng Yaping Cheng Alexander Chepurnov Davide Chiesa Pietro Chimenti Artem Chukanov Anna Chuvashova Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Salvatore Costa Flavio Dal Corso Christophe De La Taille Jiawei Deng Zhi Deng Ziyan Deng Wilfried Depnering Marco Diaz Xuefeng Ding Yayun Ding Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Georgy Donchenko Jianmeng Dong Damien Dornic Evgeny Doroshkevich Marcos Dracos Frédéric Druillole Shuxian Du Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Lukas Fajt Donghua Fan Lei Fan Can Fang Jian Fang Marco Fargetta Anna Fatkina Dmitry Fedoseev Vladko Fekete Li-Cheng Feng Qichun Feng Richard Ford Andrey Formozov Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Alexandre Göttel Christoph Genster Marco Giammarchi Agnese Giaz Nunzio Giudice Franco Giuliani Maxim Gonchar Guanghua Gong Hui Gong Oleg Gorchakov Yuri Gornushkin Marco Grassi Christian Grewing Maxim Gromov Vasily Gromov Minghao Gu Xiaofei Gu Yu Gu Mengyun Guan Nunzio Guardone Maria Gul Cong Guo Jingyuan Guo Wanlei Guo Xinheng Guo Yuhang Guo Paul Hackspacher Caren Hagner Ran Han Yang Han Miao He Wei He Tobias Heinz Patrick Hellmuth Yuekun Heng Rafael Herrera Daojin Hong YuenKeung Hor Shaojing Hou Yee Hsiung Bei-Zhen Hu Hang Hu Jianrun Hu Jun Hu Shouyang Hu Tao Hu Zhuojun Hu Chunhao Huang Guihong Huang Hanxiong Huang Qinhua Huang Wenhao Huang Xingtao Huang Yongbo Huang Jiaqi Hui Wenju Huo Cédric Huss Safeer Hussain Antonio Insolia Ara Ioannisian Daniel Ioannisyan Roberto Isocrate Kuo-Lun Jen Xiaolu Ji Xingzhao Ji Huihui Jia Junji Jia Siyu Jian Di Jiang Xiaoshan Jiang Ruyi Jin Xiaoping Jing Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Kampmann Li Kang Michael Karagounis Narine Kazarian Amir Khan Waseem Khan Khanchai Khosonthongkee Patrick Kinz Denis Korablev Konstantin Kouzakov Alexey Krasnoperov Svetlana Krokhaleva Zinovy Krumshteyn Andre Kruth Nikolay Kutovskiy Pasi Kuusiniemi Tobias Lachenmaier Cecilia Landini Sébastien Leblanc Frederic Lefevre Liping Lei Ruiting Lei Rupert Leitner Jason Leung Demin Li Fei Li Fule Li Haitao Li Huiling Li Jiaqi Li Jin Li Kaijie Li Mengzhao Li Nan Li Nan Li Qingjiang Li Ruhui Li Shanfeng Li Shuaijie Li Tao Li Weidong Li Weiguo Li Xiaomei Li Xiaonan Li Xinglong Li Yi Li Yufeng Li Zhibing Li Ziyuan Li Hao Liang Hao Liang Jingjing Liang Jiajun Liao Daniel Liebau Ayut Limphirat Sukit Limpijumnong Guey-Lin Lin Shengxin Lin Tao Lin Jiajie Ling Ivano Lippi Fang Liu Haidong Liu Hongbang Liu Hongjuan Liu Hongtao Liu Hu Liu Hui Liu Jianglai Liu Jinchang Liu Min Liu Qian Liu Qin Liu Runxuan Liu Shuangyu Liu Shubin Liu Shulin Liu Xiaowei Liu Yan Liu Alexey Lokhov Paolo Lombardi Claudio Lombardo Kai Loo Chuan Lu Haoqi Lu Jingbin Lu Junguang Lu Shuxiang Lu Xiaoxu Lu Bayarto Lubsandorzhiev Sultim Lubsandorzhiev Livia Ludhova Fengjiao Luo Guang Luo Pengwei Luo Shu Luo Wuming Luo Vladimir Lyashuk Qiumei Ma Si Ma Xiaoyan Ma Xubo Ma Jihane Maalmi Yury Malyshkin Fabio Mantovani Francesco Manzali Xin Mao Yajun Mao Stefano MMari Filippo Marini Sadia Marium Cristina Martellini Gisele Martin-Chassard Agnese Martini Davit Mayilyan Axel Müller Ints Mednieks Yue Meng Anselmo Meregaglia Emanuela Meroni David Meyhöfer Mauro Mezzetto Jonathan Miller Lino Miramonti Salvatore Monforte Paolo Montini Michele Montuschi Nikolay Morozov Pavithra Muralidharan Massimiliano Nastasi Dmitry VNaumov Elena Naumova Igor Nemchenok Alexey Nikolaev Feipeng Ning Zhe Ning Hiroshi Nunokawa Lothar Oberauer Juan Pedro Ochoa-Ricoux Alexander Olshevskiy Domizia Orestano Fausto Ortica Hsiao-Ru Pan Alessandro Paoloni Nina Parkalian Sergio Parmeggiano Teerapat Payupol Yatian Pei Nicomede Pelliccia Anguo Peng Haiping Peng Frédéric Perrot Pierre-Alexandre Petitjean Fabrizio Petrucci Luis Felipe Piñeres Rico Oliver Pilarczyk Artyom Popov Pascal Poussot Wathan Pratumwan Ezio Previtali Fazhi Qi Ming Qi Sen Qian Xiaohui Qian Hao Qiao Zhonghua Qin Shoukang Qiu Muhammad Rajput Gioacchino Ranucci Neill Raper Alessandra Re Henning Rebber Abdel Rebii Bin Ren Jie Ren Taras Rezinko Barbara Ricci Markus Robens Mathieu Roche Narongkiat Rodphai Aldo Romani Bedřich Roskovec Christian Roth Xiangdong Ruan Xichao Ruan Saroj Rujirawat Arseniy Rybnikov Andrey Sadovsky Paolo Saggese Giuseppe Salamanna Simone Sanfilippo Anut Sangka Nuanwan Sanguansak Utane Sawangwit Julia Sawatzki Fatma Sawy Michaela Schever Jacky Schuler Cédric Schwab Konstantin Schweizer Dmitry Selivanov Alexandr Selyunin Andrea Serafini Giulio Settanta Mariangela Settimo Muhammad Shahzad Vladislav Sharov Gang Shi Jingyan Shi Yongjiu Shi Vitaly Shutov Andrey Sidorenkov FedorŠimkovic Chiara Sirignano Jaruchit Siripak Monica Sisti Maciej Slupecki Mikhail Smirnov Oleg Smirnov Thiago Sogo-Bezerra Julanan Songwadhana Boonrucksar Soonthornthum Albert Sotnikov Ondrej Sramek Warintorn Sreethawong Achim Stahl Luca Stanco Konstantin Stankevich DušanŠtefánik Hans Steiger Jochen Steinmann Tobias Sterr Matthias Raphael Stock Virginia Strati Alexander Studenikin Gongxing Sun Shifeng Sun Xilei Sun Yongjie Sun Yongzhao Sun Narumon Suwonjandee Michal Szelezniak Jian Tang Qiang Tang Quan Tang Xiao Tang Alexander Tietzsch Igor Tkachev Tomas Tmej Konstantin Treskov Andrea Triossi Giancarlo Troni Wladyslaw Trzaska Cristina Tuve Stefan van Waasen Johannes van den Boom Guillaume Vanroyen Nikolaos Vassilopoulos Vadim Vedin Giuseppe Verde Maxim Vialkov Benoit Viaud Cristina Volpe Vit Vorobel Lucia Votano Pablo Walker Caishen Wang Chung-Hsiang Wang En Wang Guoli Wang Jian Wang Jun Wang Kunyu Wang Lu Wang Meifen Wang Meng Wang Ruiguang Wang Siguang Wang Wei Wang Wenshuai Wang Xi Wang Xiangyue Wang Yangfu Wang Yaoguang Wang Yi Wang Yifang Wang Yuanqing Wang Yuman Wang Zhe Wang Zheng Wang Zhimin Wang Zongyi Wang Apimook Watcharangkool Lianghong Wei Wei Wei Yadong Wei Liangjian Wen Christopher Wiebusch Steven Chan-Fai Wong Bjoern Wonsak Diru Wu Fangliang Wu Qun Wu Wenjie Wu Zhi Wu Michael Wurm Jacques Wurtz Christian Wysotzki Yufei Xi Dongmei Xia Yuguang Xie Zhangquan Xie Zhizhong Xing Benda Xu Donglian Xu Fanrong Xu Jilei Xu Jing Xu Meihang Xu Yin Xu Yu Xu Baojun Yan Xiongbo Yan Yupeng Yan Anbo Yang Changgen Yang Huan Yang Jie Yang Lei Yang Xiaoyu Yang Yifan Yang Haifeng Yao Zafar Yasin Jiaxuan Ye Mei Ye Ugur Yegin Frédéric Yermia Peihuai Yi Xiangwei Yin Zhengyun You Boxiang Yu Chiye Yu Chunxu Yu Hongzhao Yu Miao Yu Xianghui Yu Zeyuan Yu Chengzhuo Yuan Ying Yuan Zhenxiong Yuan Ziyi Yuan Baobiao Yue Noman Zafar Andre Zambanini Pan Zeng Shan Zeng Tingxuan Zeng Yuda Zeng Liang Zhan Feiyang Zhang Guoqing Zhang Haiqiong Zhang Honghao Zhang Jiawen Zhang Jie Zhang Jingbo Zhang Peng Zhang Qingmin Zhang Shiqi Zhang Tao Zhang Xiaomei Zhang Xuantong Zhang Yan Zhang Yinhong Zhang Yiyu Zhang Yongpeng Zhang Yuanyuan Zhang Yumei Zhang Zhenyu Zhang Zhijian Zhang Fengyi Zhao Jie Zhao Rong Zhao Shujun Zhao Tianchi Zhao Dongqin Zheng Hua Zheng Minshan Zheng Yangheng Zheng Weirong Zhong Jing Zhou Li Zhou Nan Zhou Shun Zhou Xiang Zhou Jiang Zhu Kejun Zhu Honglin Zhuang Liang Zong Jiaheng Zou | 2021 | Chinese Physics C2021，45，2： | 0 |
| 8 | A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model显示文摘Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%. | Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 9 | Efficacy and safety of thermobalancing therapy with Dr Allen’s Device for chronic low back pain:A randomised controlled trial显示文摘BACKGROUND Lumbar disc herniation and non-specific low back pain are common conditions that seriously affect patients’health-related quality of life(HRQoL).Although empirical evidence has demonstrated that novel Thermobalancing therapy and Dr Allen’s Device can relieve chronic low back pain,there have been no randomised controlled trials for these indications.AIM To evaluate the efficacy of Dr Allen’s Device in lumbar disc herniation(LDH)and non-specific low back pain(NSLBP).METHODS A randomised clinical trial was conducted investigating 55 patients with chronic low back pain due to LDH(n=28)or NSLBP(n=27),out of which 15 were randomly assigned to the control group and 40 were assigned to the treatment group.The intervention was treatment with Dr Allen’s Device for 3 mo.Changes in HRQoL were assessed using the Numerical Pain Rating Scale and the Japanese Orthopedic Association Back Pain Questionnaire.RESULTS Thermobalancing therapy with Dr Allen’s Device showed a significant reduction in pain in the treatment group(P<0.001),with no recorded adverse effects.Both pain assessment scales showed a significant improvement in patients’perception of pain indicating improvement in HRQoL.CONCLUSION The out-of-hospital use of Thermobalancing therapy with Dr Allen’s Device for Low Back Treatment relieves chronic low back pain significantly and without adverse effects,improves the level of activity and HRQoL among patients with LDH and NSLBP.This study demonstrates the importance of this safe first-line therapy that can be used for effective at-home management of chronic low back pain. | Simon Allen Abid Rashid Ariana Adjani Muhammad Akram Fahad Said Khan Rehan Sherwani Muhammad Talha Khalil | 2023 | World Journal of Orthopedics2023，14，12： | 0 |
| 10 | Smart Devices Based Multisensory Approach for Complex Human Activity Recognition显示文摘Sensors based Human Activity Recognition(HAR)have numerous applications in eHeath,sports,fitness assessments,ambient assisted living(AAL),human-computer interaction and many more.The human physical activity can be monitored by using wearable sensors or external devices.The usage of external devices has disadvantages in terms of cost,hardware installation,storage,computational time and lighting conditions dependencies.Therefore,most of the researchers used smart devices like smart phones,smart bands and watches which contain various sensors like accelerometer,gyroscope,GPS etc.,and adequate processing capabilities.For the task of recognition,human activities can be broadly categorized as basic and complex human activities.Recognition of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart watches.Other reasons include lack of sensor-based labeled dataset having several complex human daily life activities.Some of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition,whereas a few of them used both pocket and wrist positions.In this research,we have proposed a novel framework which is capable to recognize both basic and complex human activities using builtin-sensors of smart phone and smart watch.We have considered 25 physical activities,including 20 complex ones,using smart device’s built-in sensors.To the best of our knowledge,the existing literature consider only up to 15 activities of daily life. | Muhammad Atif Hanif Tallha Akram Aamir Shahzad Muhammad Attique Khan Usman Tariq Jung-In Choi Yunyoung Nam Zanib Zulfiqar | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 11 | HybridHR-Net:Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework显示文摘The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar actions and complex backgrounds.The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information.This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net.On a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model.Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model.Instead of fully connected layer features,we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach.Using a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning classifiers.Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,respectively.Additionally,a comparison of the proposed framework with contemporarymethods is done to demonstrate the increase in accuracy. | Muhammad Naeem Akbar Seemab Khan Muhammad Umar Farooq Majed Alhaisoni Usman Tariq Muhammad Usman Akram | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 12 | Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network显示文摘Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them. | Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 13 | Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning显示文摘Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area. | Muhammad Waleed Pasha Mir Yasir Umair Alina Mirza Faizan Rao Abdul Wakeel Safia Akram Fazli Subhan Wazir Zada Khan | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 14 | Mechanical sensor PDLIM5 promotes the osteogenesis of human adipose-derived stem cells through microfilament alterations显示文摘Bone regeneration is a multifaceted,abstract,and wellcoordinated physiological progression of bone formation that participates in continuous regeneration and remodeling throughout life.However,when it comes to complex clinical situations requiring extensive bone regeneration,such as massive bone defects caused by injuries,infection,or tumor removal,traditional methods do not often yield good treatment strategies or protocols due to their limitations. | Yuchao Yang Shutong Wu Rongmei Qu Congrong Wang Jinyang Wang Asmat Ullah Khan You Pan Wenqing Liu Jinhui Zhu Muhammad Akram Khan Chujiang Xu Jingxing Dai Jun ouyang | 2024 | Genes & Diseases2024，11，3： | 0 |
| 15 | Smart Energy Management System Using Machine Learning显示文摘Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate. | Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
| 16 | Classication of COVID-19 CT Scans via Extreme Learning Machin显示文摘Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective. | Muhammad Attique Khan Abdul Majid Tallha Akram Nazar Hussain Yunyoung Nam Seifedine Kadry Shui-Hua Wang Majed Alhaisoni | 2021 | Computers, Materials & Continua2021，，7： | 0 |
| 17 | Computer Decision Support System for Skin Cancer Localization and Classification显示文摘In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively. | Muhammad Attique Khan Tallha Akram Muhammad Sharif Seifedine Kadry Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，7： | 0 |
| 18 | Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine显示文摘Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods. | Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
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