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Editorial for Special Issue on Brain-inspired Machine Learning

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Machine Intelligence Research 2022年第5期19卷 347-349页

作者： Zhao-Xiang Zhang Bin Luo Jin Tang Shan Yu amir hussain Institute of Automation Chinese Academy of Sciences Anhui University Edinburgh Napier University

The recent years have witnessed significant progress on artificial intelligence(AI).Undoubtedly,one of the essential elements that contribute to the prosperity of current AI is brain-inspired machine learning.By introducing structural principles and operational mechanisms of the human brain into the design of computing algorithms and devices.

关键词： artificial computing introducing

Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques

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Computers, Materials & Continua 2022年第8期72卷 3195-3211页

作者： Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem amir hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan School of Computer Science University of SydneySydneyNSW 2006Australia Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

Heart disease is one of the leading causes of death in the world today.Prediction of heart disease is a prominent topic in the clinical data processing.To increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical field.There are many studies on the prediction of heart disease,but limited work is done on the selection of features.The selection of features is one of the best techniques for the diagnosis of heart diseases.In this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease prediction.For performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation techniques.The results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior performance.The selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through cross-validation.Naive Bayes and Random Forest are found to outperform other classification approaches when accurately evaluated.The results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other state-of-the-artmethods.Therefore,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease.

关键词： Heart disease brute force machine learning feature selection

A novel visual attention method for target detection from SAR images

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Chinese Journal of Aeronautics 2019年第8期32卷 1946-1958页

作者： Fei GAO Aidong LIU Kai LIU Erfu YANG amir hussain School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Space Mechatronic Systems Technology Laboratory Department of DesignManufacture and Engineering ManagementUniversity of StrathclydeGlasgow G1 1XJUK Cognitive Big Data and Cyber-Informatics(CogBID)Laboratory School of ComputingEdinburgh Napier UniversityEdinburgh EH10 5DTScotlandUK

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

关键词： Learning strategy Synthetic Aperture Radar(SAR) images Target detection Top-down Visual attention mechanism

A Study on Neonatal Tolerance Against Graves＇ Disease in BALB/c Mice

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Chinese Medical Journal 2015年第23期128卷 3243-3246页

作者： Li-PingWu Li-Ru Xun Li Xu amir hussain Bing-Yin Shi Department of Endocrinology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi 710061 China Department of Nephrology Shaanxi Province People's Hospital Xi'an Shaanxi 710068 China

INTRODUCTION Autoimmunity is defined as, a condition characterized by a specific humoral or cell-mediated immune response against the constituents of the body＇s own tissues （autoantigens）. In numerous autoimmune diseases, such an immune response is well recognized that causes damage to the self-constituents of body tissues by the products of the immune system. Graves＇ hyperthyroidism occurs after the loss of tolerance to the thyroid stimulating hormone receptor （TSHR） and the generation of thyroid stimulatory antibodies that mimic the action of thyroid-stimulating hormone （TSH）.

关键词： Animal Model Graves' Disease Immune Tolerance Thyroid Stimulating Hormone Receptor

A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 64-76页

作者： Cosimo Ieracitano Annunziata Paviglianiti Maurizio Campolo amir hussain Eros Pasero Francesco Carlo Morabito the DICEAM University Mediterranea of Reggio Calabria89124 Reggio CalabriaItaly Politecnico of Turin 10129 TurinItaly the School of Computing Edinburgh Napier UniversityEdinburgh EH105DTUK

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.

关键词： Anomaly detection autoencoder(AE) electrospinning machine learning material informatics nanomaterials

A Novel Refined Track Initiation Algorithm for Group Targets Based on Group Model

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Chinese Journal of Electronics 2014年第4期23卷 851-856页

作者： GAO Fei REN He WANG Jun amir hussain Tariq S. Durrani School of Electronic and Information Engineering Beihang University School of Natural Sciences University of Stirling Department of Electronic & Electrical Engineering University of Strathclyde

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that,compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.

关键词： Group targets Track initiation Group model State equation Data association

BitmapAligner:Bit-Parallelism String Matching with MapReduce and Hadoop

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Computers, Materials & Continua 2021年第9期68卷 3931-3946页

作者： Mary Aksa Junaid Rashid Muhammad Wasif Nisar Toqeer Mahmood Hyuk-Yoon Kwon amir hussain Department of Computer Science COMSATS University IslamabadWah47040Pakistan Department of Computer Science AIR University IslamabadAerospace and Aviation Campus Kamra43570Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Industrial Engineering and Research Center for Electrical and Information Technology Seoul National University of Science and TechnologyNowon-GuSeoul01811Korea Data Science and Cyber Analytics Research Group Edinburgh Napier UniversityEdinburghEH114DYUK

Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome data.The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics.The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis.This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file system.The proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment processing.The Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by default.BitmapAligner can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the data.The experimental results indicate that BitmapAligner operates in real time,with a large number of sequences.Moreover,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query sequence.The MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)algorithm.The dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache Hadoop.Conversely,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’positions.Moreover,the proposed model resolves the largescale data challenges that are covered through MapReduce in the Ha

关键词： Next-generation sequencing multiple sequence alignment MapReduce Hadoop whole-genome big data bit-parallelism

Target detection and recognition in SAR imagery based on KFDA

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Journal of Systems Engineering and Electronics 2015年第4期26卷 720-731页

作者： Fei Gao Jingyuan Mei Jinping Sun Jun Wang Erfu Yang amir hussain School of Electronic and Information Engineering Beihang University Space Mechatronic Systems Technology Laboratory Department of Design Manufacture and Engineering Management University of Strathclyde Cognitive Signal-Image and Control Processing Research Laboratory School of Natural SciencesUniversity of Stirling

Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

关键词： synthetic aperture radar （SAR） target detection ker-nel fisher discriminant analysis （KFDA） target recognition imageEuclidean distance （IMED） support vector machine （SVM）.

Deep Learning Based Single Image Super-resolution:A Survey

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International Journal of Automation and computing 2019年第4期16卷 413-426页

作者： Viet Khanh Ha Jin-Chang Ren Xin-Ying Xu Sophia Zhao Gang Xie Valentin Masero amir hussain Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1XW UK College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan 030024 China Department of Computer Systems and Telematics Engineering University of Extremadura Badajoz 06006 Spain School of Computing Edinburgh Napier University Edinburgh EH10 5DT UK School of Computer Science and Technology Anhui University Anhui 230039 China

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing " the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.

关键词： Image super-resolution convolutional neural network high-resolution image low-resolution image deep learning