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| 1 | Reduction of phenolics content and COD in olive oil mill wastewaters by indigenous yeasts and fungi显示文摘 | Halah Aissam Michel J. Penninckx Mohamed Benlemlih | 2007 | World Journal of Microbiology and Biotechnology2007，，9： | 1 |
| 2 | Complexity analysisof EEG in patients with schizophrenia using fractal dimension 显示文摘 | Raghavendra BS Dutt DN Halahalli HN | 2009 | Physiol Meas2009，30，8： | 1 |
| 3 | Reduction ofphenolies content and COD in olive oil mill wastewaters by in-digenous yeasts and fungi显示文摘 | Halah Aissam Michel J Penninckx Mohamed Benlemlih | 2007 | World Journal of Microbiology and Biotechnology2007，，5： | 1 |
| 4 | Reduction of phenolics content and COD in olive oil mill wastewaters by indigenous yeasts and fungi 显示文摘 | Halah Aissam Michel J Penninckx Mohamed Benlemlih | 2007 | World Journal of Microbiology and Biotechnology2007，5，： | 1 |
| 5 | Nardostachysjatamansi extract prevents chronic restraintstress-induced learning and memory deficits in a radialarm maze task显示文摘 | Karkada G Shenoy K B Halahalli H | 2012 | Journal of Natural Science Biology and Medicine2012，3，： | 1 |
| 6 | Fusion Based Tongue Color Image Analysis Model for Biomedical Applications显示文摘Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe.Recently,several deep learning(DL)based tongue color image analysis models have existed in the literature for the effective detection of diseases.This paper presents a fusion of handcrafted with deep features based tongue color image analysis(FHDF-TCIA)technique to biomedical applications.The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model,and thereby determines the existence of disease.Primarily,the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise.The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns(LBP)withMobileNet based deep features for the generation of optimal feature vectors.In addition,the political optimizer based quantum neural network(PO-QNN)based classification technique has been utilized for determining the proper class labels for it.A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992. | Esam A.AlQaralleh Halah Nassif Bassam A.Y.Alqaralleh | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 7 | 梅西百货如何打赢圣诞零售大战显示文摘当各大卖场还在与亚马逊等购物网站争得不可开交时. | Halah touryalai 李雨蒙 | 2014 | 中国民商2014，，2： | 0 |
| 8 | Smart Deep Learning Based Human Behaviour Classification for Video Surveillance显示文摘Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922. | Esam A.Al.Qaralleh Fahad Aldhaban Halah Nasseif Malek Z.Alksasbeh Bassam A.Y.Alqaralleh | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 9 | Hybrid Metaheuristics Based License Plate Character Recognition in Smart City显示文摘Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods. | Esam A.Al.Qaralleh Fahad Aldhaban Halah Nasseif Bassam A.Y.Alqaralleh Tamer AbuKhalil | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 10 | Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles显示文摘Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial institutions.One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine attacks.This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy.The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution.The research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information.The results suggest that detecting the submarine early increases the likelihood of averting a collision.The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency.Securing communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the task.The swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its peers.This,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other UAVs.The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target. | Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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