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| 1 | Fire-retardant-treated low-formaldehyde-emission particleboard made from recycled wood-waste显示文摘 | WANG Songyung YANG Tehsin LIN Liting | | 0，，06： | 1 |
| 2 | Diabetic wounds and artificial intelligence:A mini-review显示文摘Diabetic wound takes longer time to heal due to micro and macro-vascular ailment.This longer healing time can lead to infections and other health complications.Foot ulcers are one of the most common diabetic wounds.These are one of the leading cause of amputations.Medical science is continuously striving for improving quality of human life.A recent trend of amalgamation of knowledge,efforts and technological advancement of medical science experts and artificial intelligence researchers,has made tremendous success in diagnosis,prognosis and treatment of a variety of diseases.Diabetic wounds are no exception,as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner.The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science,for diabetic wound healing.Framework for diabetic wound assessment using artificial intelligence is presented.Moreover,this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients.The article also discusses the future directions for the betterment of the field that can lead to facilitate both,clinician and patients. | Samabia Tehsin Sumaira Kausar Amina Jameel | 2023 | World Journal of Clinical Cases2023，11，1： | 0 |
| 3 | Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches显示文摘The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not efficient.To the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records.In this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization.Fuzz-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes.We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets.The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility. | Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon | 2023 | Digital Communications and Networks2023，9，5： | 0 |
| 4 | Classification of Bone Marrow Cells for Medical Diagnosis of Acute Leukemia显示文摘Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood cells.Physicians rely on their experience to determine the type and subtype of Leukemia from the blood sample.Most people are misdiagnosed when it comes to its subtypes,the error rates can be up to 40%during the classification process.That too depends on the expertise of the physician.This research represents a Convolutional Neural Network based medical image classifier.The proposed technique can classify Leukemia and its five subtypes.State of the art deep learning and transfer learning techniques have been used,all of the network architectures are fine-tuned followed by feature extraction.This research can assist the pathologist in decision making or can be used for the process automation.The proposed methodology can give better results in terms of accuracy and is more effective than the hematologist’s visual classification in terms of time. | Khadija Khan Samabia Tehsin | 2022 | Journal on Artificial Intelligence2022，4，1： | 0 |
| 5 | Multi-Scale Network for Thoracic Organs Segmentation显示文摘Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately. | Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 6 | A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding显示文摘In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.Scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene etc.and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes Dataset.Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way. | Abdul Majid Sumaira Kausar Samabia Tehsin Amina Jameel | 2022 | Computer Systems Science & Engineering2022，43，10： | 0 |
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