-
An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-22 Subhashini Ramachandran, Rajasekar Velusamy, Namakkal Venkataraman Srinivasan Sree Rathna Lakshmi, Chakaravarthi Sivanandam
Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast an...
-
A robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-22 Meenal Thayumanavan, Asokan Ramasamy
Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and...
-
Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-22 Bala Subramanian Chokkalingam, Balakannan Sirumulasi Paramasivan, Maragatharajan Muthusamy
Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and n...
-
Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-16 Chukwuebuka Joseph Ejiyi, Zhen Qin, Grace Ugochi Nneji, Happy Nkanta Monday, Victor K. Agbesi, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Olusola O. Bamisile
Aiming at early detection and accurate prediction of cardiovascular disease (CVD) to reduce mortality rates, this study focuses on the development of an intelligent predictive system to identify in...
-
Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-09 Muthulakshmi Karuppiyan, Hariharan Subramani, Shanthy Kandasamy Raju, Manimekalai Maradi Anthonymuthu Prakasam
The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptabili...
-
New results on bifurcation for fractional-order octonion-valued neural networks involving delays* Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-04-05 Changjin Xu, Jinting Lin, Yingyan Zhao, Qingyi Cui, Wei Ou, Yicheng Pang, Zixin Liu, Maoxin Liao, Peiluan Li
This work chiefly explores fractional-order octonion-valued neural networks involving delays. We decompose the considered fractional-order delayed octonion-valued neural networks into equivalent re...
-
Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-03-21 Chukwuebuka Joseph Ejiyi, Zhen Qin, Chiagoziem Chima Ukwuoma, Grace Ugochi Nneji, Happy Nkanta Monday, Makuachukwu Bennedith Ejiyi, Thomas Ugochukwu Ejiyi, Uchenna Okechukwu, Olusola O. Bamisile
Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations),...
-
Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-03-14 E.V.R.M. Kalaimani Shanmugham, Saravanan Dhatchnamurthy, Prabbu Sankar Pakkiri, Neha Garg
An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. T...
-
Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-03-06 Sangeetha Alagumani, Uma Maheswari Natarajan
The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other ...
-
A Spinal MRI Image Segmentation Method Based on Improved Swin-UNet Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-03-03 Jie Cao, Jiacheng Fan, Chin-Ling Chen, Zhenyu Wu, Qingxuan Jiang, Shikai Li
As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propo...
-
Stable route selection for adaptive packet transmission in 5G-based mobile communications Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-03-03 Muthulakshmi Karuppiyan, Hariharan Subramani, Karthick Raj Shanthy, Mani Anand Pandiyan Manimekalai
The poor connectivity among mobile nodes introduces uncertainty in packet loss as the path link is not measured in this network. The focus is placed on communication cost to achieve valid packet tr...
-
Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-24 N. Ananthi, V. Balaji, M. Mohana, S. Gnanapriya
Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of sm...
-
Hybrid Sneaky algorithm-based deep neural networks for Heart sound classification using phonocardiogram Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-08 Rajveer K. Shastri, Aparna R Shastri, Prashant P Nitnaware, Digambar M Padulkar
In the diagnosis of cardiac disorders Heart sound has a major role, and early detection is crucial to safeguard the patients. Computerized strategies of heart sound classification advocate intensiv...
-
Brain tumour classification using MRI images based on lenet with golden teacher learning optimization Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-08 Srilakshmi Aluri, Sagar S Imambi
Brain tumour (BT) is a dangerous neurological disorder produced by abnormal cell growth within the skull or brain. Nowadays, the death rate of people with BT is linearly growing. The finding of tum...
-
Automated grape leaf nutrition deficiency disease detection and classification Equilibrium Optimizer with deep transfer learning model Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-08 Vaishali Bajait, Nandagopal Malarvizhi
Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification ut...
-
Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-08 Dr. Dhanabal Subramanian, Sangeetha Subramaniam, Krishnamoorthy Natarajan, Kumaravel Thangavel
Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main l...
-
Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-02-12 Rathinavelu Sathiyaseelan, Krishnamoorthy Ravi, Ramesh Ramamoorthy, Mithun Pedda Chennaiah
Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based ...
-
Bolstering IoT security with IoT device type Identification using optimized Variational Autoencoder Wasserstein Generative Adversarial Network Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-01-31 Jothi Shri Sankar, Saravanan Dhatchnamurthy, Anitha Mary X, Keerat Kumar Gupta
Due to the massive growth in Internet of Things (IoT) devices, it is necessary to properly identify, authorize, and protect against attacks the devices connected to the particular network. In this ...
-
Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-01-31 Selvarani Pandiyan, Veera Keerthika, Sathish Surendran, Sundar Ravi
This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates m...
-
M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-01-27 Premalatha Gurumurthy, Manjunathan Alagarsamy, Sangeetha Kuppusamy, Niranjana Chitra Ponnusamy
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar...
-
Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-01-15 Mukesh Kumar Tripathi, Shivendra
This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic m...
-
State identification for a class of uncertain switched systems by differential neural networks Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2024-01-11 Isaac Chairez, Alejandro Garcia-Gonzalez, Alberto Luviano-Juarez
This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural ...
-
Optimization-enabled deep learning model for disease detection in IoT platform Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-12-28 Amol Dattatray Dhaygude
Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspe...
-
Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-12-28 Gnanendra Kotikam, Lokesh Selvaraj
The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besid...
-
CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-12-05 Ying Yang, Shengbin Yue, Haiyan Quan
Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricat...
-
Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-21 Dev Femi, Manoj Ananad Mukunthan
Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification d...
-
A clustering approach for attack detection and data transmission in vehicular ad-hoc networks Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-18 Atul Barve, Pushpinder Singh Patheja
Vehicular ad-hoc networks (VANETs) are increasingly pivotal for empowering applications in smart cities and intelligent traffic systems. However, the reliability and stability of VANET communicatio...
-
Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-20 Fatma Özcan, Ahmet Alkan
Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. It is thought that the tempor...
-
Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-09 Sudhakar Raju, Venkateswara Rao Peddireddy Veera
Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential shapes, and image intensities, segmentation of the...
-
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-09 M. Masthan, K. Pazhanikumar, Meena Chavan, Jyothi Mandala, Sanjay Nakharu Prasad Kumar
Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detecti...
-
Optimization of data pre-processing methods for time-series classification of electroencephalography data Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-09 Christoph Anders, Gabriel Curio, Bert Arnrich, Gunnar Waterstraat
The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neurona...
-
How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-09 Xinwei Wang, Hongliang Gao, Jiulong Song, Peng Jing, Chao Wang, Nuanxin Yu, Shanshan Wu, Jianxiong Zhu, Zhiqiang Gao
The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousnes...
-
MDSR-NMF: Multiple deconstruction single reconstruction deep neural network model for non-negative matrix factorization Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-11-09 Prasun Dutta, Rajat K. De
Dimension reduction is one of the most sought-after strategies to cope with high-dimensional ever-expanding datasets. To address this, a novel deep-learning architecture has been designed with mult...
-
A study of neural artistic style transfer models and architectures for Indian art styles Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-09-05 J Mercy Faustina, V Akash, Anmol Gupta, V Divya, Takasi Manoj, N Sadagopan, B Sivaselvan
Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing meth...
-
Taylor-Gorilla troops optimized deep learning network for surface roughness estimation Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-08-22 Syed Jahangir Badashah, Shaik Shafiulla Basha, Shaik Rafi Ahamed, S P V Subba Rao, M Janardhan Raju, Mudda Mallikarjun
In order to guarantee the desired quality of machined products, a reliable surface roughness assessment is essential. Using a surface profile metre with a contact stylus, which can produce accurate...
-
KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-08-03 G. Jasmine Christabel, A.C. Subhajini
The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a pred...
-
A novel approach for neural networks based diagnosis and grading of stroke in tumor-affected brain MRIs Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-06-23 Somasundaram Krishnamoorthy, Sivakumar Paulraj, Nagendra Prabhu Selvaraj, Balakumaresan Ragupathy, Selvapandian Arumugam
ABSTRACT Recognition and diagnosis of stroke from magnetic resonance Image (MRIs) are significant for medical procedures in therapeutic standards. The primary goal of this scheme is the discovery of stroke in tumour locale in brain tissues influenced image. The probability of stroke is categorized on brain tumour influenced images into mild, moderate, or serious cases. The mild and moderate phases
-
A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-05-29 Yongsheng Wang, Yuhao Wu, Hao Xu, Zhen Chen, Jing Gao, ZhiWei Xu, Leixiao Li
ABSTRACT Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore
-
Shorter TR combined with finer atlas positively modulate topological organization of brain network: A resting state fMRI study Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-05-22 Yan Zhang, Qili Hu, Jiali Liang, Zhenghui Hu, Tianyi Qian, Kuncheng Li, Xiaohu Zhao, Peipeng Liang
ABSTRACT Background: The use of shorter TR and finer atlases in rs-fMRI can provide greater detail on brain function and anatomy. However, there is limited understanding of the effect of this combination on brain network properties. Methods: A study was conducted with 20 healthy young volunteers who underwent rs-fMRI scans with both shorter (0.5s) and long (2s) TR. Two atlases with different degrees
-
Basal ganglia network dynamics and function: Role of direct, indirect and hyper-direct pathways in action selection Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-03-01 Jian Song, Hui Lin, Shenquan Liu
ABSTRACT Basal ganglia (BG) are a widely recognized neural basis for action selection, but its decision-making mechanism is still a difficult problem for researchers. Therefore, we constructed a spiking neural network inspired by the BG anatomical data. Simulation experiments were based on the principle of dis-inhibition and our functional hypothesis within the BG: the direct pathway, the indirect
-
Solving time delay fractional optimal control problems via a Gudermannian neural network and convergence results Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-02-24 Farzaneh Kheyrinataj, Alireza Nazemi, Marziyeh Mortezaee
ABSTRACT In this paper, we propose a Gudermannian neural network scheme to solve optimal control problems of fractional-order system with delays in state and control. The fractional derivative is described in the Caputo sense. The problem is first transformed, using a Padé approximation, to one without a time-delayed argument. We try to approximate the solution of the Hamiltonian conditions based on
-
Two phases based training method for designing codewords for a set of perceptrons with each perceptron having multi-pulse type activation function Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2023-01-10 Ziyin Huang, Bingo Wing-Kuen Ling, Yui-Lam Chan
ABSTRACT This paper proposes a two phases-based training method to design the codewords to map the cluster indices of the input feature vectors to the outputs of the new perceptrons with the multi-pulse type activation functions. Our proposed method is applied to classify two types of the tachycardias. First, the total number of the new perceptrons is initialized as the dimensions of the input feature
-
FMTM-feature-map-based transform model for brain image segmentation in tumor detection Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-12-13 Revathi Sundarasekar, Ahilan Appathurai
ABSTRACT The segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles. This impacts the outcome
-
A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-11-24 Saravanan Suba, M. Muthulakshmi
ABSTRACT COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS – Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human
-
Extraction of the association rules from artificial neural networks based on the multiobjective optimization Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-10-19 Dounia Yedjour, Hayat Yedjour, Samira Chouraqui
ABSTRACT Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the fact that they do not justify their answers. The lack of transparency on how ANN makes decisions motivated us to develop our rule extraction algorithm
-
Rulkov neural network coupled with discrete memristors Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-10-06 Yanmei Lu, Chunhua Wang, Quanli Deng
ABSTRACT The features of memristive-coupled neural networks have been studied extensively in the continuous field. However, the particularities of the discrete domain are rarely mentioned. This paper constructs a discrete memristor with sine-type conductance and applies the discrete memristor to coupling the Rulkov neuron maps for the first time. The properties of the proposed memristive-coupled bi-neuron
-
A smoothing gradient-based neural network strategy for solving semidefinite programming problems Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-08-04 Asiye Nikseresht, Alireza Nazemi
ABSTRACT Linear semidefinite programming problems have received a lot of attentions because of large variety of applications. This paper deals with a smooth gradient neural network scheme for solving semidefinite programming problems. According to some properties of convex analysis and using a merit function in matrix form, a neural network model is constructed. It is shown that the proposed neural
-
Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-07-12 Yadavendra, Satish Chand
ABSTRACT The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net model, for the segmentation of the cell nucleus, a critical functional unit that determines the function and structure of the body. The nucleus contains
-
Spiking model of fixational eye movements and figure-ground segmentation Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-05-25 August Romeo, Hans Supèr
ABSTRACT We present a model connecting eye movements and cortical state. Its structure includes simulated retinal images, motion detection, feature detectors and layers of spiking neurons. The designed scheme shows how the effect of micro-saccadic scale eye movements can lead to successful figure segregation in a figure-ground paradigm, by inducing changes in the neural dynamics through the time evolution
-
Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-04-25 K. Kalaivani, N. Uma Maheswari, R. Venkatesh
ABSTRACT Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy. To solve this, the present study proposes a hybridized Ant Lion Crow Search Optimization Genetic Algorithm
-
Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier. Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-04-25 K Kalaivani,N Uma Maheswari,R Venkatesh
Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy. To solve this, the present study proposes a hybridized Ant Lion Crow Search Optimization Genetic Algorithm (ALCSOGA)
-
Training of artificial neural networks with the multi-population based artifical bee colony algorithm Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-04-21 Cihat Kirankaya, Latife Gorkemli Aykut
ABSTRACT Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers’ acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is
-
On delay optimal control problems with a combination of conformable and Caputo-Fabrizio fractional derivatives via a fractional power series neural network Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-04-17 Farzaneh Kheyrinataj, Alireza Nazemi
ABSTRACT This paper presents a class of linear and nonlinear delay optimal control problems with mixed control-state constraints using a conformable fractional derivative. We modify the conformable fractional derivative using a novel translation from Caputo-Fabrizio derivative where the kernel is replaced by a suitable exponential function. Using some properties of the modified conformable derivative
-
A framework for preparing a stochastic nonlinear integrate-and-fire model for integrated information theory Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-04-05 Hossein Mojarrad, Vahid Azimirad, Behrooz Koohestani
ABSTRACT This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and after-spike refractoriness. The noise is modelled as an additive term in the system’s equations. By preparing the model for the IIT analysis
-
A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-02-23 Erol Egrioglu, Eren Bas
ABSTRACT The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothesis tests for using single multiplicative neuron model artificial neural networks for forecasting purposes like classical time series forecasting
-
Whether Mirror and Conceptual Neurons are Myths? Sparse vs. Distributed Neuronal Representations Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-01-24 Wieslaw Galus
ABSTRACT Multi-layer neural networks, mirror neurons, and gnostic neurons are concepts that assign neural representations to mental representations of percepts and inner sensations. However, none of these approaches alone can explain the higher mental functions, which we observe in natural minds from the third and first-person perspectives through introspection. Recent concepts of preservation of chemical
-
Whether Mirror and Conceptual Neurons are Myths? Sparse vs. Distributed Neuronal Representations. Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-01-24 Wieslaw Galus
Multi-layer neural networks, mirror neurons, and gnostic neurons are concepts that assign neural representations to mental representations of percepts and inner sensations. However, none of these approaches alone can explain the higher mental functions, which we observe in natural minds from the third and first-person perspectives through introspection. Recent concepts of preservation of chemical traces
-
Evaluation of shape factor impact on discharge coefficient of side orifices using boost simulation model with extreme learning machine data-driven Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-01-09 Majeid Heydari, Saeid Shabanlou, Babak San Ahmadi
ABSTRACT In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme Learning Machine (ELM) model. In addition, the Monte Carlo simulations (MCs) are applied to assess the accuracy of the modelling. Furthermore, the validation is conducted by means of the k-fold cross-validation approach (with k = 5). In other
-
A multilayer perceptron neural network approach for the solution of hyperbolic telegraph equations Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2022-01-03 Shagun Panghal, Manoj Kumar
ABSTRACT Neural networks have been extensively used for solving differential equations in the past, but they rely mostly on computationally expensive gradient-based numerical optimization procedure for solving differential equations. In this work, we are introducing a faster way to train neural networks for solving differential equations based on extreme learning machine algorithm. This algorithm is
-
Solving infinite-horizon optimalcontrol problems of the time-delayedsystems by a feed forward neural network model Netw. Comput. Neural. Syst. (IF 7.8) Pub Date : 2021-04-19 Alireza Nazemi, Ensieh Fayyazi
ABSTRACT A numerical method using neural network for solving infinite-horizon time-delayed optimal control problems is studied. The problem is first transformed, using a Páde approximation, to one without a time-delayed argument. By a suitable change of variable, the obtained non-delay infinite-horizon optimal control problem is converted to a finite-horizon nonlinear optimal control problem. We try