scholarly journals EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 7 ◽  
Author(s):  
S Subasree ◽  
N P Gopalan ◽  
N K Sakthivel
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Research Work ◽  
Time Processing ◽  
Multi Objective ◽  
Time Sensitivity ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Cancer Pattern ◽  
Memory Utilization

Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

HEp-2 CELL CLASSIFICATION BY ADAPTIVE CONVOLUTIONAL LAYER BASED CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 31 (06) ◽  
pp. 1950044
Author(s):  
C. C. Manju ◽  
M. Victor Jose
Keyword(s):  
Neural Network ◽  
Autoimmune Diseases ◽  
Convolutional Neural Network ◽  
Research Work ◽  
Test Case ◽  
Morphological Operations ◽  
Cell Classification ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Opening And Closing

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process. Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN. Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures. Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

scholarly journals Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

2021 ◽  
Vol 15 (4) ◽  
pp. 1-18
Author(s):  
Gururaj T. ◽  
Siddesh G. M.
Keyword(s):  
Processing Time ◽  
Gene Expression Analysis ◽  
Gene Sequence ◽  
Hybrid Approach ◽  
Basic Operation ◽  
Sequence Matching ◽  
Hadoop Mapreduce ◽  
Proposed Model ◽  
Performance Issues ◽  
Memory Utilization

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

scholarly journals C3IMD : An Efficient Class-Based Clustering Classifier for Im-putation Intelligent Medical Data

2018 ◽  
Vol 7 (2.27) ◽  
pp. 255
Author(s):  
P. Premalatha ◽  
S Subasree ◽  
N K Sakthivel
Keyword(s):  
Classification Accuracy ◽  
Missing Values ◽  
Research Work ◽  
Medical Application ◽  
Medical Data ◽  
Data Sets ◽  
Data Generation ◽  
Huge Amount ◽  
Sensitivity Specificity ◽  
Fast Evolution

The fast evolution in medical application yields to abundance of huge amount of data in volume and velocity.  Due to this heterogeneous medical data generation from clinical trials, its typically not free from missing values.  Previously introduced imputation techniques don’t discourse the high spatiality problems and application of distance function that even have curse on high spatiality problem. Thus, there’s a necessity an Efficient and Accurate technique to overcome this problem in Medical Data Analysis. To address the above mentioned issues, this research work proposed an efficient Class-Based Clustering Classifier for Imputation Intelligent Medical Data (C3IMD).  This work was implemented in Bio Weka and studied thoroughly. To improve the classification and prediction accuracy, missing data in Medical Data Sets were filled efficiently with the help of proposed Cluster-Classifier Model. The experiments are repeated with various datasets and results are evaluated and compared with existing classifiers WPT-DELM and SVM-DELM. From the results obtained, it was revealed that the proposed Class-Based Clustering Classifier for Imputation Intelligent Medical Data (C3IMD) is outperforming both the existing models in terms of Classification Accuracy, Sensitivity, Specificity and FScore.  

scholarly journals A Joint Model for Macular Edema Analysis in Optical Coherence Tomography Images Based on Image Enhancement and Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhifu Tao ◽  
Wenping Zhang ◽  
Mudi Yao ◽  
Yuanfu Zhong ◽  
Yanan Sun ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Edema ◽  
Image Enhancement ◽  
Level Set ◽  
Processing Time ◽  
Similarity Coefficient ◽  
Kappa Coefficient ◽  
Manual Delineation ◽  
Proposed Model ◽  
Sensitivity Specificity

Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.

scholarly journals Parallel framework based gene signature-hierarchical random forest cluster for predicting human diseases

2018 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
N K Sakthivel ◽  
N P Gopalan ◽  
S Subasree
Keyword(s):  
Random Forest ◽  
Processing Time ◽  
Research Work ◽  
Gene Signature ◽  
Human Diseases ◽  
Memory Usage ◽  
Common Neighbor ◽  
Proposed Model ◽  
Disease Pattern ◽  
Neighbor Classifier

Gene is not responsible for many Human Diseases and instead, diseases occur by different or group of genomes interacting together and cause diseases. Hence it is need to analyse and associate the complete genome sequences to understand or predict various possible human diseases. This research work focused i. Hierarchical-Random Forest based Clustering (HRF-Cluster), ii. Genetic Algorithm-Gene Associa-tion Classifier (GA-GA) and iii. Weighted Common Neighbor Classifier (wCN). These Classifiers were implemented and studied thor-oughly in terms of Prediction Accuracy, Memory Utilization, Memory Usage and Processing Time. To improve the performances of the Gene Classifiers / Predictors further, this research work was proposed and implemented Gene Signature based HRF Cluster, G-HR. Re-sults show that that the performances of the proposed Classifier G-HR is outperforming as compared with the identified three Classifiers in terms of Disease Pattern Prediction, Processing Time, Memory Usage and Classification Accuracy. To improve the performance of the system further in term of Processing Time, the proposed model G-HR is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time de-creases considerably which will improve the performance of the Proposed Model. 

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

scholarly journals Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Processing Time ◽  
Gene Expression Analysis ◽  
Gene Sequence ◽  
Hybrid Approach ◽  
Basic Operation ◽  
Sequence Matching ◽  
Hadoop Mapreduce ◽  
Proposed Model ◽  
Performance Issues ◽  
Memory Utilization

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

scholarly journals Design of a 2-Bit Neural Network Quantizer for Laplacian Source

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 933
Author(s):  
Zoran Perić ◽  
Milan Savić ◽  
Nikola Simić ◽  
Bojan Denić ◽  
Vladimir Despotović
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Processing Time ◽  
Computing Power ◽  
Popular Approach ◽  
Network Applications ◽  
High Classification Accuracy ◽  
Proposed Model ◽  
Uniform Quantization ◽  
Neural Network Applications

Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation by applying quantization techniques is a popular approach to facilitate this issue. Here, we analyze in detail and design a 2-bit uniform quantization model for Laplacian source due to its significance in terms of implementation simplicity, which further leads to a shorter processing time and faster inference. The results show that it is possible to achieve high classification accuracy (more than 96% in the case of MLP and more than 98% in the case of CNN) by implementing the proposed model, which is competitive to the performance of the other quantization solutions with almost optimal precision.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Towards a Multi Objective Path Optimisation for Car Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Ching-Sheng Chiu ◽  
Chris Rizos
Keyword(s):  
Selection Criteria ◽  
Empirical Studies ◽  
Route Selection ◽  
Multi Objective ◽  
Optimal Paths ◽  
Proposed Model ◽  
Shortest Distance ◽  
Shortest Path Algorithms ◽  
Car Navigation System ◽  
Car Navigation

In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

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