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| 1 | 镉胁迫对不同水稻基因型植株生长和抗氧化酶系统的影响显示文摘以籽粒镉积累水平不同的两种品种 (丙 972 5 2 ,低积累型 ;秀水 6 3,高积累型 )为材料 ,研究了镉胁迫对水稻植株生长和抗氧化酶系统的影响。采用水培试验 ,镉处理设 0 .0、0 .1、1.0和 5 .0 μmol/L 4个水平。结果表明 ,镉胁迫抑制植株生长和叶绿素合成 ,改变植株丙二醛 (MDA)含量和超氧物歧化酶 (SOD)、过氧化氢酶 (CAT)、过氧化物酶 (POD)活性。在抗氧化酶活性上 ,根和地上部对镉胁迫的反应存在着差异。总体上 ,SOD、CAT和POD活性随镉水平的提高而减少 ,而MDA含量则表现相反。根和地上部MDA含量随着培养液中镉浓度提高而增加 ,且增加幅度秀水 6 3明显大于丙 972 5 2。与对照相比 ,生长在 5 .0 μmol/LCd处理下的植株 ,SOD活性在孕穗期下降 4 6 %~ 5 2 % ,在分蘖期仅下降 13%~ 19%。高浓度镉胁迫下 ,两品种在MDA含量的增加幅度和叶绿素含量的降低幅度上表现不同 ,显示出它们对镉的耐性存在着差异。 | 邵国胜 MUHAMMAD Jaffar Hassan 章秀福 张国平 | 2004 | 中国水稻科学2004，18，3： | 107 |
| 2 | Personal Review:Sources of sulfide in waste streams and current biotechnologies for its removal显示文摘Sulfide-containing waste streams are generated by a number of industries. It is emitted into the environment as dis- solved sulfide (S2- and HS-) in wastewaters and as H2S in waste gases. Due to its corrosive nature, biological hydrogen sulfide removal processes are being investigated to overcome the chemical and disposal costs associated with existing chemically based removal processes. The nitrogen and sulfur metabolism interacts at various levels of the wastewater treatment process. Hence, the sulfur cycle offers possibilities to integrate nitrogen removal in the treatment process, which needs to be further optimized by appropriate design of the reactor configuration, optimization of performance parameters, retention of biomass and optimization of biomass growth. The present paper reviews the biotechnological advances to remove sulfides from various environments. | MAHMOOD Qaisar ZHENG Ping CAI Jing HAYAT Yousaf HASSAN Muhammad Jaffar WU Dong-lei HU Bao-lan | 2007 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2007，8，7： | 22 |
| 3 | Wheat straw pretreatment with KOH for enhancing biomethane production and fertilizer value in anaerobic digestion显示文摘Wheat straw biodegradability during anaerobic digestion was improved by treatment with potassium hydroxide(KOH)to decrease digestion time and enhance biomethane production and fertility value.KOH concentrations of1%(K1),3%(K2),6%(K3)and 9%(K4)were tested for wheat straw pretreatment at ambient temperature with a C:N ratio of 25:1.86% of total solids(TS),89% of volatile solids(VS)and 22% of lignocellulose,cellulose and hemicellulose(LCH)(22%)were decomposed effectively with the wheat straw pretreated by 6% KOH.Enhanced biogas production and cumulative biomethane yield of 258 ml·(g VS)^(-1)were obtained increased by 45% and 41%respectively,compared with untreated wheat straw.Pretreated wheat straw digestion also yielded a digestate with higher fertilizer values potassium(138%),calcium(22%)and magnesium(16%).These results show that TS,VS and LCH can be effectively removed from wheat straw pretreated with KOH,improving biodegradability biomethane production and fertilizer value. | Muhammad Jaffar Yunzhi Pang Hairong Yuan Dexun Zou Yanping Liu Baoning Zhu Rashid Mustafa Korai Xiujin Li | 2016 | Chinese Journal of Chemical Engineering2016，24，3： | 11 |
| 4 | Regulation of flowering time in chrysanthemum by the R2R3 MYB transcription factor CmMYB2 is associated with changes in gibberellin metabolism显示文摘The switch from vegetative growth to reproductive growth is a key event in the development of a plant.Here,the product of the chrysanthemum gene CmMYB2,an R2R3 MYB transcription factor that is localized in the nucleus,was shown to be a component of the switching mechanism.Plants engineered to overexpress CmMYB2 flowered earlier than did wild-type plants,while those in which CmMYB2 was suppressed flowered later.In both the overexpression and RNAi knockdown plants,a number of genes encoding proteins involved in gibberellin synthesis or signaling,as well as in the response to photoperiod,were transcribed at a level that differed from that in the wild type.Both yeast two-hybrid and bimolecular fluorescence complementation assays revealed that CmMYB2 interacts with CmBBX24,a zinc-finger transcription factor known to regulate flowering by its influence on gibberellin synthesis. | Lu Zhu Yunxiao Guan Yanan Liu Zhaohe Zhang Muhammad Abuzar Jaffar Aiping Song Sumei Chen Jiafu Jiang Fadi Chen | 2020 | Horticulture Research2020，7，1： | 5 |
| 5 | Effect of Phosphorus Deficiency on Leaf Photosynthesis and Carbohydrates Partitioning in Two Rice Genotypes with Contrasting Low Phosphorus Susceptibility显示文摘To study the effect of phosphorus (P) deficiency on leaf photosynthesis and carbohydrates partitioning and to determine whether the characteristics of leaf photosynthesis and carbohydrates partitioning are related to low P tolerance in rice plants, a hydroponic culture experiment supplied with either sufficient P (10 mg /L) or deficient P (0.5 mg /L) was conducted by using two rice genotypes different in their responses to low P stress. Results showed that the plant growth of Zhenongda 454 (low P tolerant genotype) was less affected by P deficiency compared with Sanyang’ai (low P sensitive genotype). Under P-deficient conditions, photosynthetic rates of Zhenongda 454 and Sanyang’ai were decreased by 16% and 35%, respectively, and Zhenongda 454 showed higher photosynthetic rate than Sanyang’ai. Phosphorus deficiency decreased the stomatal conductance for both genotypes, but had no significant influence on leaf internal CO2 concentration (Ci), suggesting that the decrease in leaf photosynthetic rate of rice plants induced by P deficiency was not due to stomatal limitation. Phosphorus deficiency increased the concentration of soluble carbohydrates and sucrose in shoots and roots for both genotypes, and also markedly increased the allocation of soluble carbohydrates and sucrose to roots. Under deficient P supply, Zhenongda 454 had higher root/shoot soluble carbohydrates content ratio and root/shoot sucrose content ratio than Sanyang’ai. In addition, phosphorus deficiency increased the concentration of starch in roots for both genotypes, whereas had no effect on the content of starch in shoots or roots. Compared to genotype Sanyang’ai, the better tolerance to low-P stress of Zhenongda 454 can be explained by the fact that Zhenongda 454 maintains a higher photosynthetic rate and a greater ability to allocate carbohydrates to the roots under P deficiency. | LI Yong-fu Luo An-cheng Muhammad Jaffar HASSAN WEI Xing-hua | 2006 | Rice science2006，13，4： | 2 |
| 6 | Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks显示文摘Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality. | QAISAR MAHMOOD PING ZHENG DONG-LEI WU XU-SHENG WANG HAYAT YOUSAF EJAZ UL-ISLAM MUHAMMAD JAFFAR HASSAN GHULAM JILANI MUHAMMAD RASHID AZIM | 2007 | Biomedical and Environmental Sciences2007，20，5： | 1 |
| 7 | Influence of Cadmium Toxicity on Growth and Antioxidant Enzyme Activity in Rice Cultivars with Different Grain Cadmium Accumulation显示文摘 | Muhammad Jaffar Hassan Guosheng Shao Guoping Zhang | 2005 | Journal of Plant Nutrition2005，，7： | 1 |
| 8 | Effect of Organic and Inorganic Phosphorous on Growth of Roselle(Hibiscus sabdariffa L.)显示文摘To investigate the performance of organic and inorganic phosphorous on growth of Roselle(Hibiscus sabdariffa L.),an experiment was carried out at Newly Developmental Farm(NDF),Horticulture Section,the university of Agriculture Peshawar,Pakistan,during 2012.The experiment was laid out in a Randomized Complete Block Design(RCBD) with split plot arrangement and replicated three times.The chemical fertilizer Single Super Phosphate(SSP) was used as inorganic source of phosphorus,while Organic Phosphate(OP) from organic product produced by Niha corporation containing 20%organic mineralized P_2O_5,was used as the source of organic phosphorous.Both the sources of fertilizer were applied at the rate of 10,20,30 40 and 50 kg · hm^(-2) and control.The statistical analysis of data revealed that both the sources and levels of phosphorus significantly affected most of the growth parameters of Roselle except germination percentage and survival percentage.Plant height(112.09 cm),numbers of leave plant^(-1)(177.87),numbers of branch plant^(-1)(4.08),leaf areas(126.60 cm^2),days to flowering(142.83),fresh flower weight(2.56 g),fresh calyx weight(1.70 g),dry calyx weight(0.43 g),numbers of calyces(94.10),numbers of root plant^(-1)(11.03),root length(22.67 cm)and root weight(27.50 g) were observed in plants fertilized with organic source of phosphorous.Phosphorous levels significantly affected all of the parameters except germination percentage and survival percentage.The maximum plant height(124.39 cm),numbers of branches(5.32),numbers of leave plant^(-1)(204.89),leaf areas(148.14 cm^2),fresh flower weight(3.32 g),fresh calyx weight(2.04 g),dry calyx weight(0.51 g),numbers of calyces(105.30),numbers of root plant^(-1)(12.78),root length(24.50 cm) and root weight(29.94 g) were observed in plants fertilized with 40 kg·hm^(-2) phosphorous and the maximum numbers of days to flowering(148.17) were observed in the control plants.It was concluded from the experimental results that organic phosphorous at the rate40 kg·hm^(-2) would be used for better production of Roselle under agro-climatic condition of Peshawar. | Hasnain Alam Muhammad Razaq Salahuddin Jaffar Khan | 2016 | Journal of Northeast Agricultural University(English Edition)2016，23，3： | 1 |
| 9 | Zinc alleviates growth inhibition and oxidative stress caused by cadmium in rice 显示文摘 | Muhammad Jaffar Hassan Guoping Zhang Feibo Wu et aI | 2005 | Journal of Plant Nutrition Soil Science2005，168，： | 1 |
| 10 | Data-Driven Probabilistic S for Batsman Performance Prediction in a Cricket Match显示文摘Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success.A good batsman not only scores run but also provides stability to the team’s innings.The most important factor in selecting a batsman is their ability to score runs.It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record.This hypothesis is based on the fact that a player’s batting aver-age is generally considered to be a good indicator of their future performance.We proposed a data-driven probabilistic system for batsman performance prediction in the game of cricket.It captures the dependencies between the runs scored by a batsman in consecutive balls.The system is evaluated using a dataset extracted from the Cricinfo website.The system is based on a Hidden Markov model(HMM).HMM is used to generate the prediction model to foresee players’upcoming performances.The first-order Markov chain assumes that the probabil-ity of a batsman scoring runs in the next ball is only dependent on how many runs he scored in the current ball.We use a data-driven approach to learn the para-meters of the HMM from data.A probabilistic matrix is made that predicts what scores the batter can do on the upcoming balls.The results show that the system can accurately predict the runs scored by a batsman in a ball. | Fawad Nasim Muhammad Adnan Yousaf Sohail Masood Arfan Jaffar Muhammad Rashid | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 11 | Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion显示文摘Due to limited depth-of-field of digital single-lens reflex cameras,the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred(out-of-focus)in the image.Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene.In this paper,a new Fuzzy Based Hybrid Focus Measure(FBHFM)for multi-focus image fusion has been proposed.Optimal block size is very critical step for multi-focus image fusion.Particle Swarm Optimization(PSO)algorithm has been used to find optimal size of the block of the images for extraction of focus measure features.After finding optimal blocks,three focus measures Sum of Modified Laplacian,Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique.Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image.Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm(GA),Principal Component Analysis(PCA),Laplacian Pyramid discrete wavelet transform(DWT),and aDWT for image fusion.It has been found that proposed method performs well as compare to existing methods. | Muhammad Ahmad M.Arfan Jaffar Fawad Nasim Tehreem Masood Sheeraz Akram | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 12 | Infectious causes of fever of unknown origin in developing countries: An international ID-IRI study显示文摘Background:Fever of unknown origin(FUO)in developing countries is an important dilemma and further research is needed to elucidate the infectious causes of FUO.Methods:A multi-center study for infectious causes of FUO in lower middle-income countries(LMIC)and lowincome countries(LIC)was conducted between January 1,2018 and January 1,2023.In total,15 participating centers from seven different countries provided the data,which were collected through the Infectious DiseasesInternational Research Initiative platform.Only adult patients with confirmed infection as the cause of FUO were included in the study.The severity parameters were quick Sequential Organ Failure Assessment(qSOFA)≥2,intensive care unit(ICU)admission,vasopressor use,and invasive mechanical ventilation(IMV).Results:A total of 160 patients with infectious FUO were included in the study.Overall,148(92.5%)patients had community-acquired infections and 12(7.5%)had hospital-acquired infections.The most common infectious syndromes were tuberculosis(TB)(n=27,16.9%),infective endocarditis(n=25,15.6%),malaria(n=21,13.1%),brucellosis(n=15,9.4%),and typhoid fever(n=9,5.6%).Plasmodium falciparum,Mycobacterium tuberculosis,Brucellae,Staphylococcus aureus,Salmonella typhi,and Rickettsiae were the leading infectious agents in this study.A total of 56(35.0%)cases had invasive procedures for diagnosis.The mean qSOFA score was 0.76±0.94{median(interquartile range[IQR]):0(0–1)}.ICU admission(n=26,16.2%),vasopressor use(n=14,8.8%),and IMV(n=10,6.3%)were not rare.Overall,38(23.8%)patients had at least one of the severity parameters.The mortality rate was 15(9.4%),and the mortality was attributable to the infection causing FUO in 12(7.5%)patients.Conclusions:In LMIC and LIC,tuberculosis and cardiac infections were the most severe and the leading infections causing FUO. | Hakan Erdem Jaffar AAl-Tawfiq Maha Abid Wissal Ben Yahia George Akafity Manar Ezzelarab Ramadan Fatma Amer Amani El-Kholy Atousa Hakamifard Bilal Ahmad Rahimi Farouq Dayyab Hulya Caskurlu Reham Khedr Muhammad Tahir Lysien Zambrano Mumtaz Ali Khan Aun Raza Nagwa Mostafa El-Sayed Magdalena Baymakova Aysun Yalci Yasemin Cag Umran Elbahr Aamer Ikram | 2024 | Journal of Intensive Medicine2024，4，1： | 0 |
| 13 | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling显示文摘Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations. | Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla | 2023 | Journal of Rock Mechanics and Geotechnical Engineering2023，15，3： | 0 |
| 14 | A Process Oriented Integration Model for Smart Health Services显示文摘Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its residents.Smart integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing urbanization.Currently,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efficient collection and sharing of data,along with inadequate collaboration of software and hardware.Aiming to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling technique.Using this integration modeling solution,atfirst,the service participants,processes and tasks of smart services are identified and then standard illustrations are developed for the better understand-ing of the integrated service group environment.Business process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart home.The research is concluded with the integration process model application for the required data sharing among different service groups.The outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated. | Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 15 | Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine显示文摘Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung.It is mostly caused by the instinctive growth of cells in the lung.Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan images.Early detection plays an important role in the survival rate and treatment of lung cancer patients.Moreover,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer.This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine(SVM)algorithm namely LungNet-SVM.The proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract features.Support vector machine classifier is applied for the binary classification of nodules into benign andmalignant.The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer.The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy. | Iftikhar Naseer Tehreem Masood Sheeraz Akram Arfan Jaffar Muhammad Rashid Muhammad Amjad Iqbal | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 16 | Hybrid Color Texture Features Classification Through ANN for Melanoma显示文摘Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques. | Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 17 | Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions显示文摘Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm. | Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 18 | Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net显示文摘The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms. | Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 19 | A Multi-Modal Deep Learning Approach for Emotion Recognition显示文摘In recent years,research on facial expression recognition(FER)under mask is trending.Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task.The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face,however,a multi-modal technique can be employed to generate better results.We proposed a multi-modal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions.The multimodal has been trained on a facial and vocal dataset.We have used two standard datasets,M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal expressions.The vocal expressions are in the form of audio while the faces data is in image form that is why the data is heterogenous.In order to make the data homogeneous,the voice data is converted into images by taking spectrogram.A spectrogram embeds important features of the voice and it converts the audio format into the images.Later,the dataset is passed to the multimodal for training.neural network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models. | H.M.Shahzad Sohail Masood Bhatti Arfan Jaffar Muhammad Rashid | 2023 | Intelligent Automation & Soft Computing2023，，5： | 0 |
| 20 | Intelligent Sound-Based Early Fault Detection System for Vehicles显示文摘An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features. | Fawad Nasim Sohail Masood Arfan Jaffar Usman Ahmad Muhammad Rashid | 2023 | Computer Systems Science & Engineering2023，46，9： | 0 |
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