State Of Art Algorithms Research Articles

Classifying promoters by interpreting the hidden information of DNA sequences for disease prediction in clinical laboratories using Gaussian decision boundary estimation

A promoter is a brief stretch of DNA (100–1,000 bp) where RNA polymerase starts to transcribe a gene. A DNA (Deoxyribonucleic Acid) base pair is a fundamental unit of DNA structure and represents the pairing of two complementary nucleotide bases within the DNA double helix. The four DNA nucleotide bases are adenine (A), thymine (T), cytosine (C), and guanine (G). DNA base pairs are the building blocks of the DNA molecule, and their complementary pairing is central to the storage and transmission of genetic information in all living organisms. Normally, a promoter is found at the 5′ end of the transcription initiation site or immediately upstream. Numerous human disorders, particularly diabetes, cancer, and Huntington’s disease, have been shown to have DNA promoter as their root cause. The scientific community has long been interested in learning crucial information about protein-coding genes. Finding the promoters is therefore the first step in finding genes in DNA sequences. The scientific world has always been attracted by the effort to glean crucial knowledge about protein-coding genes. Consequently, identifying promoters has emerged as an intriguing challenge that has caught the interest of numerous researchers in the field of bioinformatics. We proposed Gaussian Decision Boundary Estimation in machine learning models to detect transcription start sites (promoters) in the DNA sequences of a common bacteria, Escherichia coli. The best features are identified through a score-based function to select relevant nucleotides that are directly responsible for promoter recognition, in order maximise the models’ performance. The Gaussian Decision Boundary Estimation based support-vector-machine model is trained with these features and finds the best hyperplane that separates the data into different classes. Throughout this study, promoter regions could be identified with high accuracy 99.9% which is better compared to other state of art algorithms. The comparison of machine learning classification models is another major emphasis of this paper in order to identify the model that most accurately predicts DNA sequence promoters. It provides analysis for further biological research as well as precision medicine.

Vision Transformer Based Photo Capturing System

Portrait photo is one of the most crucial documents that many people need for official transactions in many public and private organizations. Despite the developing technologies and high resolution imaging devices, people need such photographer offices to fulfil their needs to take photos. In this study, a Photo Capturing System has been developed to provide infrastructure for web and mobile applications. After the system detects the person's face, facial orientation and facial expression, it automatically takes a photo and sends it to a graphical user interface developed for this purpose. Then, with the help of the user interface of the photo taken by the system, it is automatically printed out. The proposed study is a unique study that uses imaging technologies, deep learning and vision transformer algorithms, which are very popular image processing techniques in several years. Within the scope of the study, face detection and facial expression recognition are performed with a success rate of close to 100\% and 95.52\%, respectively. In the study, the performances of Vision Transformer algorithm is also compared with the state of art algorithms in facial expression recognition.

Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases

Kidney failure, also called as end-stage renal disease is the final stage of chronic kidney disease(CKD). Kidney failure cannot be revered and is the life-threatening if left untreated. Hence there is a demand for the early and automatic detection of CKD from radiology images based on deep learning techniques. This work focuses on the diagnosis of the predominant CKDs such as kidney stone, cyst and tumor from CT scan images. CT scan abdomen and urogram images are used to construct the digital-twin for the diagnosis of CKD in the robust manner. The proposed work is based on the dataset which contains 12,446 unique CT scan images, within it in which the cyst contains 3,709, normal 5,077, stone 1,377, and tumor 2,283. Deep features are extracted from these images and generated hyper edges from these features. These features are used to construct hypergraph representing the renal images. These hypergraphs are used in hypergraph convolutional neural network for representational learning. Finally, the model is validated by hold-out dataset. Deep learning metrics including precision, recall, accuracy, and FI score are used to validate the proposed model. It outperforms compared to other state of art algorithms with a superior validation accuracy of 99.71. The proposed model is a robust digital-twin for the early diagnosis of kidney diseases. It will assist the nephrologist for the better prognosis of kidney related abnormalities.

Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.

Optimal Power Flow for Distribution System using Gradient-Based Optimizer

In the distribution network, DG penetration increases prominently, and has altered the nature of the distribution network into an active and passive network. DISCOMs/DSOs are incorporating all kinds of DGs, including non-renewables and renewables now a day. If DGs are planned and controlled adequately, then it improves voltage deviation, reduces active power loss, and leads to the economic operation of the active distribution network. Efficient operation of the distribution network can be achieved by solving optimal power flow. In this work, optimal power flow (OPF) for a modified IEEE-69 bus distribution network with DGs is formulated and solved using Gradient Based Optimizer (GBO) in MATLAB 2021a. OPF is solved with objectives to minimize fuel cost, voltage profile improvement, and active power losses. The performance of GBO is compared with other state of art algorithms (PSO, ABC, GWO, and JANA). Performance analysis proves the efficacy and capability to solve real-world problems of GBO over other state of art algorithms.

Neighbor Graph Based Tensor Recovery For Accurate Internet Anomaly Detection

Detecting anomalous traffic is a crucial task for network management. Although many anomaly detection algorithms have been proposed recently, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper uses the tensor model for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low-rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). Besides only detecting random anomalies, our algorithm can also effectively detect structured anomalies that appear as bursts. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve higher Accuracy and Recall.

Designing of optimal digital IIR filter in the multi-objective framework using an evolutionary algorithm

In this work, an optimization technique i.e. diversity-driven multi-parent evolutionary algorithm with adaptive non-uniform mutation (DDMPEA-ANUM) has been used to design a digital IIR filter considering the magnitude response error of pass-band and stop-band and linear phase response error as fitness functions. The proposed algorithm utilizes population space aggregation and fitness variance concepts to guide the solution so that it will not stuck in local optima. The efficacy of the developed method is tested on twenty-three benchmark functions and compared with other state of art algorithms in terms of best, worst, average and standard deviation. The statistical analysis of the developed algorithm has also been carried out through Wilcoxon’s rank-sum to confirm its effectiveness. The developed algorithm while designing the IIR filter design problem obtained the least values for magnitude response error of pass-band and stop-band and linear phase response when compared to other state of art algorithms. For instance, the developed algorithm obtained magnitude response error for pass band and stop band and linear phase response error of 0.0101, 0.727 and 3.3903 ×10−06 for LP, 0.0073, 0.003 and 2.3933 ×10−06 for HP, 0.0018, 0.0260 and 4.4429 ×10−06 for bandpass filter and 0.0445, 0.0209 and 1.3081 ×10−06 for band stop filter.

Optimal Relay Selection Strategy for Efficient and Reliable Cluster-Based Cooperative Multi-Hop Transmission in Vehicular Communication

Routing is the key issue for Vehicular Networks since each node in the network has mobility. The dynamic nature of vehicular networks increases with mobility and the consequence is reflected in controlling traffic overhead. As a result, the routing technique and the type of node mobility information are completely dependent on building dependable end-to-end communication links. To address these challenges, a Reliable Cluster-based Multi-Hop Cooperative Routing (RCCR) strategy was proposed by using velocity, distance, and link quality parameters. This algorithm obtains the tradeoff between Quality of Service (QoS) and mobility constraints over link parameters. It improves routing scalability by electing cluster heads and selecting Multi-Point Relays (MPRs) while considering mobility constraints and QoS needs. The proposed technique determines the link quality for every pair of nodes based on values of signal strength and distance parameters. The relay vehicles are chosen based on the highest possible QoS value, which is calculated to assure route stability, reliability, and durability. The heuristic limitations of the multi-point relay selection strategy are handled by considering the link quality, distance from the source vehicle, and cluster-head coverage area to enhance the performance of the multi-hop network with tolerable End-to-End transmission delay. Further, we obtained an optimal number of cooperative vehicles in every hop. Finally, simulation results demonstrate the effectiveness of the proposed algorithm compared to other state of art algorithms.

Optimal Relay Selection Strategy for Energy-Efficient Cooperative Multi-Hop Image Transmission in Wireless Multimedia Sensor Network

For high-quality picture transmission through smart devices in Mobile Multimedia Sensor Networks (MWMSN), high transmission rates, throughput, and a low Bit Error Rate are necessary. At the same time, energy efficiency is always a big problem for battery-powered smart devices like smartphones and tablets. MWMSN makes extensive use of cooperative communication to realize the benefits of the Multiple-Input-Multiple-Output technology. Energy consumption and routing are major challenges for Cooperative Mobile Multimedia Sensor Networks since each node in the network have mobility. The dynamic nature of MWSNs increases with mobility and the consequence is reflected in the control traffic overhead. To address these challenges, a Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS) strategy was proposed by using velocity, distance, and link quality parameters. The proposed technique determines the link quality for every pair of nodes based on values of signal strength and distance parameters. The relay nodes are chosen based on the highest possible Quality of Service (QoS) value, which is calculated to assure route stability, reliability, and durability. Further, we obtained an optimal number of cooperative nodes in every hop with the objective to minimize end-to-end energy consumption. Finally, simulation results demonstrate the effectiveness of the proposed algorithm compared to other state of art algorithms.

Clustering analysis revealed the autophagy classification and potential autophagy regulators' sensitivity of pancreatic cancer based on multi-omics data.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is unresponsive to conventional therapeutic modalities due to its high heterogeneity, expounding the necessity, and priority of searching for effective biomarkers and drugs. Autophagy, as an evolutionarily conserved biological process, is upregulated in PDAC and its regulation is linked to a poor prognosis. Increased autophagy sequestered MHC-I on PDAC cells and weaken the antigen presentation and antitumor immune response, indicating the potential therapeutic strategies of autophagy inhibitors. By performing 10 state-of-the-art multi-omics clustering algorithms, we constructed a robust PDAC classification model to reveal the autophagy-related genes among different subgroups. After building a more comprehensive regulating network for potential autophagy regulators exploration, we concluded the top 20 autophagy-related hub genes (GAPDH, MAPK3, RHEB, SQSTM1, EIF2S1, RAB5A, CTSD, MAP1LC3B, RAB7A, RAB11A, FADD, CFKN2A, HSP90AB1, VEGFA, RELA, DDIT3, HSPA5, BCL2L1, BAG3, and ERBB2), six miRNAs, five transcription factors, and five immune infiltrated cells as biomarkers. The drug sensitivity database was screened based on the biomarkers to predict possible drug-targeting signal pathways, hoping to yield novel insights, and promote the progress of the anticancer therapeutic strategy. We succefully constructed an autophagy-related mRNA/miRNA/TF/Immune cells network based on a 10 state-of art algorithm multi-omics analysis, and screened the drug sensitivity dataset for detecting potential signal pathway which might be possible autophagy modulators' targets.

The inherent time complexity and an efficient algorithm for subsequence matching problem

Subsequence matching is an important and fundamental problem on time series data. This paper studies the inherent time complexity of the subsequence matching problem and designs a more efficient algorithm for solving the problem. Firstly, it is proved that the subsequence matching problem is incomputable in time O ( n 1-δ ) even allowing polynomial time preprocessing if the hypothesis SETH is true, where n is the size of the input time series and 0 ≤ δ < 1, i.e., the inherent complexity of the subsequence matching problem is ω ( n 1-δ ). Secondly, an efficient algorithm for subsequence matching problem is proposed. In order to improve the efficiency of the algorithm, we design a new summarization method as well as a novel index for series data. The proposed algorithm supports both Euclidean Distance and DTW distance with or without z -normalization. Experimental results show that the proposed algorithm is up to about 3 ~ 10 times faster than the state of art algorithm on the constrained z -normalized Euclidean Distance and DTW distance, and is up to 7 ~ 12 times faster on Euclidean Distance.

Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

Evaluating the impact of delay constraints in network services for intelligent network slicing based on SKM model

Solving the problem of network resource allocation with delay constraint is a significant challenge for realizing future Internet and 5G networks services such as advanced mobile broadband services and Internet of things (IoT), especially under the network slicing scenario. The impact of delay constraints may lead to rejection of demands, resulting in low resource utilization of network resources. This is especially severe when dynamic traffic is considered. Therefore, intelligent resource allocation algorithms are required to use the network resources in delay constrained scenario efficiently. Moreover, these algorithms should guarantee quality of service (QoS) between different priority slices during congestion case. Therefore, in this paper, we analyze the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient squatting and kicking model (SKM), proved in other works to be the most effective up to the present time yet. SKM incorporates kicking and squatting of resources as innovative techniques enabling it to achieve 100% resource utilization and acceptance ratio for higher priority slices in scenarios where the other state of art algorithms not able to reach by far in some scenarios. Simulation results showed that incorporating delay constraints has a significant impact on the performance, resulting in up to 10% and 4% reduction in terms of average resource utilization and acceptance ratios respectively.

A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein–Protein Interaction Networks (PPINs)

Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

Predicting temperature of Erbil City applying deep learning and neural network

<span>One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on <span id="docs-internal-guid-850bd062-7fff-c6c8-9146-ba6427eb24e0" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">long short-term memory</span> (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and <span id="docs-internal-guid-14d37b98-7fff-0f76-848f-ad9f89224f77" style="font-size: 9pt; font-family: 'Times New Roman'; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> generalized regression neural network</span> (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.</span>

An efficient low bit rate image watermarking and tamper detection for image authentication

This study presents the vulnerability of digital documents and its effective way to protect the ownership and detection of unauthorized modification of multimedia data. Watermarking is an effective way to protect vulnerable data in a digital environment. In this paper, a watermarking algorithm has been proposed based on a lossy compression algorithm to ensure authentication and detection of forgery. In this proposed method, the CDF9/7 biorthogonal wavelet is used to transform the watermark image and encoded the wavelet coefficients using Set Partition in Hierarchical Tree algorithm. Then, the encoded bits are encrypted by shuffling and encrypting using symmetric keys. After that the encrypted bits are inserted into the Least Significant Bit position of the cover image. In addition, two tamper detection bits are generated based on texture information and pixel location and inserted in the watermarked image. The proposed algorithm reconstructs the watermark and the tampering region more efficiently and achieved 56.5463 dB PSNR for STARE database. Experimental result shows that the proposed algorithm is effectively prevented different attacks and ensure the integrity of watermark bits within the watermarked image. Also finds the tampered region more efficiently compared with the existing state of art algorithms.

New collocation path-following approach for the optimal shape parameter using Kernel method

The goal of this work is to develop a numerical method combining Radial Basic Functions (RBF) kernel and a high order algorithm based on Taylor series and homotopy continuation method. The local RBF approximation applied in strong form allows us to overcome the difficulties of numerical integration and to treat problems of large deformations. Furthermore, the high order algorithm enables to transform the nonlinear problem to a set of linear problems. Determining the optimal value of the shape parameter in RBF kernel is still an outstanding research topic. This optimal value depends on density and distribution of points and the considered problem for e.g. boundary value problems, integral equations, delay-differential equations etc. These have been extensively attempts in literature which end up choosing this optimal value by tests and error or some other ad-hoc means. Our contribution in this paper is to suggest a new strategy using radial basis functions kernel with an automatic reasonable choice of the shape parameter in the nonlinear case which depends on the accuracy and stability of the results. The computational experiments tested on some examples in structural analysis are performed and the comparison with respect to the state of art algorithms from the literature is given.

Sparse dictionary analysis via structure frequency response spectrum model for weak bearing fault diagnosis

Rolling element bearings are the critical parts of every rotating machinery and their failure is one of the main reasons for the machine downtime and even breakdown. It is a big challenge to extract the weak transient impulse related to fault features of rolling bearing under strong background noise. Dictionary Learning (DL) based on Sparse Representation Theory (SRT), an effective means in handling this problem, has received tremendous attention both in academia and industry, However, the methods based on DL inevitably has its own drawbacks in either experimental or engineering implementation in terms of anti-interference, the great majority of methods fail to achieve a good balance between the robustness of anti-noise and the generalization. Therefore, a novel sparse structure frequency analysis framework based on DL is proposed to address this problem in this paper, A K-SVD(generalizing the K-means clustering process) based DL algorithm is firstly introduced to capture the inner structure information of the row fault signal, then a novel Sparse Frequency Response Spectrum Model (SFRSM) is proposed to expose the fault information with sparse frequency structure graph (SFSG) in a straightforward and detailed manner, and a new global filtering feature extraction algorithm called frequency response function editing filtering (AFEF) is also proposed to obtain relevant fault features masked by noise. Simulation analysis validates the effectiveness of the method. The experimental result based on the bearing fault test bed demonstrate the superiority of the proposed method in terms of anti-noise and adaptability compared with other state of art algorithms.

A Novel Nonlinear Expanded Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization Problems

Multi-objective optimization problems exist widely in scientific research and engineering applications. With the number of objectives increasing, the proportion of non-dominated individuals in the population of many-objective optimization problems increases sharply, resulting in a reduction of convergence pressure of the traditional multi-objective optimization algorithms. In some cases, the optimal solutions may be located in the special regions, such as many discrete regions and the regions with very few feasible solutions. In this case, the existing nonlinear expanded evolutionary algorithm can not find the true Pareto fronts. To address the limitation, a novel nonlinear expanded dominance relation based many-objective evolutionary algorithm is proposed to handle many-objective optimization problems. Experimental results show that compared with the state of art algorithms, the proposed algorithm is effective for DTLZs, in terms of IGD, PD and GD metrics.

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

Recently Noninvasive Electroencephalogram (EEG) systems are gaining much attention. Brain-computer Interface (BCI) systems rely on EEG analysis to identify the mental state of the user, change in cognitive state and response to the events. Motor Execution (ME) is a very important control paradigm. This paper introduces a robust and useful User-Independent Hybrid Brain-computer Interface (UIHBCI) model to classify signals from fourteen EEG channels that are used to record the reactions of the brain neurons of nine subjects. Through this study the researchers identified relevant multisensory features of multi-channel EEG that represent the specific mental processes depending on two different evaluation models (Audio/Video) and (Male/Female). The Deep Belief Network (DBN) was applied independently on the two models where, the overall achieved classification rates were better in ME classification compared to the state of art. For evaluation four models were tested in addition to the proposed model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Brain-computer Interface Lower-Limb Motor Recovery (BCI LLMR) and Hybrid Steady-State Visual Evoked Potential Rapid Serial Visual Presentation Brain-computer Interface (Hybrid SSVEP-RSVP BCI). Results indicated the proposed model, LDA, SVM, BCI LLMR and Hybrid SSVEP-RSVP BCI accuracies for (A/V) model are 94.44%, 66.67%, 61.11%, 83.33% and 89.67% respectively, while for (M/F) model, the overall accuracies are 94.44%, 88.89%, 83.331%, 85.44% and 89.45%. Finally, the proposed model achieved superiority over the state of art algorithms in both (A/V) and (M/F) models.