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نتیجه جستجو - رنامه نویسی ژنتیک

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN
تشخیص شخصی نوار قلب ECG مبتنی بر وابستگی های عملکردی و ساختاری سیگنالها با استفاده از نمایش فرکانس زمان و CNN مورفولوژیکی تکاملی-2021
Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks
مقاله انگلیسی
2 کارایی بیت کوین: یک رویکرد برنامه نویسی ژنتیکی قوی برای بازارهای الکترونیکی هوشمند بیت کوین
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 47
از زمانی که بیت کوین برای اولین بار توسط ساتوشی ناکاموتو در سال 2008 پیشنهاد شد، ارزهای دیجیتال توجه زیادی را به خود جلب کردند و پتانسیل ایفای نقش مهمی در تجارت الکترونیک را برجسته کردند. با این حال، اطلاعات نسبتا کمی در مورد ارزهای دیجیتال، رفتار قیمتی آنها، سرعت ترکیب اطلاعات جدید و کارایی بازار مربوطه آنها وجود دارد. برای گسترش ادبیات فعلی در این زمینه، ما چهار بازار هوشمند بیت کوین الکترونیکی را با انواع مختلف معامله گران با استفاده از یک فرم تطبیقی خاص از الگوریتم یادگیری مبتنی بر برنامه نویسی ژنتیکی تایپ شده قوی (STGP) توسعه می دهیم. ما تکنیک STGP را برای داده های تاریخی بیت کوین در فرکانس های یک دقیقه و پنج دقیقه اعمال می کنیم تا شکل گیری پویایی بازار بیت کوین و کارایی بازار را بررسی کنیم. از طریق انبوهی از روش‌های تست قوی، متوجه می‌شویم که هر دو بازار بیت‌کوین پر از معامله‌گران با فرکانس بالا (HFT) در فرکانس یک دقیقه کارآمد هستند اما در فرکانس پنج دقیقه ناکارآمد هستند. این یافته از این استدلال حمایت می کند که در فرکانس یک دقیقه سرمایه گذاران می توانند اطلاعات جدید را به شیوه ای سریع و منطقی ترکیب کنند و از نویز مرتبط با فرکانس پنج دقیقه رنج نبرند. ما همچنین با نشان دادن اینکه معامله‌گران با هوش صفر نمی‌توانند به کارایی بازار برسند، به ادبیات تجارت الکترونیک کمک می‌کنیم، بنابراین شواهدی علیه فرضیه هی ارائه می‌کنیم. یکی از پیامدهای عملی این مطالعه این است که ما نشان می‌دهیم که متخصصان تجارت الکترونیک می‌توانند از ابزارهای هوش مصنوعی مانند STGP برای انجام پروفایل بازار مبتنی بر رفتار استفاده کنند.
کلمات کلیدی: هوش مصنوعی | بازارهای الکترونیک هوشمند | تجارت بیت کوین | ارزهای دیجیتال | محاسبات تکاملی | کارایی بازار
مقاله ترجمه شده
3 Evolutionary hash functions for specific domains
توابع هش تکاملی برای دامنه های خاص-2019
Hash functions are a key component of many essential applications, ranging from compilers, databases or internet browsers to videogames or network devices. The same reduced set of functions are extensively used and have become ‘‘standard de facto’’ since they provide very efficient results in searches over unsorted sets. However, depending on the characteristics of the data being hashed, the overall performance of these non-cryptographic hash functions can vary dramatically, becoming a very common source of performance loss. Hash functions are difficult to design, they are extremely non-linear and counterintuitive, and relationships among variables are often intricate and obscure. Surprisingly, very little scientific research is devoted to the design and experimental assessment of these widely used functions. In this work, in addition to performing an up-to-date comparison of state-of-the-art hash functions, we propose the use of evolutionary techniques for designing ‘‘ad hoc’’ non-cryptographic hash functions. Thus, genetic programming will be used to automatically design a tailor-made hash function that can be continuously evolved if needed, so that it is always adapted to real-world dynamic environments. To validate the proposed approach, we have compared several quality metrics for the generated functions and the most widely used non-cryptographic hash functions across eight different scenarios. The results of the evolved hash functions outperformed those of the non-cryptographic hash functions in most of the cases tested.
Keywords: Genetic programming | Hash functions | Evolutionary algorithm | Automated design
مقاله انگلیسی
4 An evolutionary framework for machine learning applied to medical data
یک چارچوب تکاملی برای یادگیری ماشین که برای داده های پزشکی کاربرد دارد-2019
Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.
Keywords: Machine learning | Logical rule induction | Data mining | Supervised learning | Evolutionary computation | Genetic programming | Ensemble classifier | Medical data
مقاله انگلیسی
5 Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia
Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia-2019
Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachy- cardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle differ- ent tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved.
Keywords: Physiological signal classification | Heart disease | Neuroevolution | Probabilistic grammar | Genetic programming | Deep neural network
مقاله انگلیسی
6 A massively parallel Grammatical Evolution technique with OpenCL
تکنیک تکاملی گرامری موازی انبوه با OpenCL-2017
Grammatical Evolution (GE) is a bio-inspired metaheuristic capable of evolving programs in an arbitrary language using a formal grammar. Among the major applications of the technique, the automatic inference of models from data can be highlighted. As with other genetic programming techniques, GE has a high computational cost. However, the algorithm has steps that can be computed independently, enabling the use of parallel computing to reduce the execution time and, consequently, making it possible its application to larger and more complex problems. Here, models of massively parallel computation for GE are studied and proposed using OpenCL, a framework for the creation of parallel algorithms in heterogeneous computing environments. Computational experiments were conducted to analyze the performance of an implementation using GPUs (Graphics Processing Units), when compared to a sequential implementation in CPUs (Central Processing Units). Finally, speedups of up to 528× were achieved, when all steps are performed in parallel in a GPU.
Keywords: Grammatical evolution | Genetic programming | Parallel computing | OpenCL
مقاله انگلیسی
7 Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data
برنامه نویسی ژنتیک و کاوش مجموعه موارد مکرر به منظور شناسایی الگوهای انتخاب پارامتر داده های صرع iEEG و FMRI-2015
Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.& 2014 Elsevier Ltd. All rights reserved.
Keywords: Frequent itemset mining | Genetic programming | Feature selection | iEEG | fMRI | Epilepsy
مقاله انگلیسی
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