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Kiruthiga, G.
- Energy Efficient Load Balancing Aware Task Scheduling in Cloud Computing using MultiObjective Chaotic Darwinian Chicken Swarm Optimization
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Authors
Affiliations
1 PG and Research Department of Computer Science, PresidencyCollege, Chennai, Tamil Nadu, IN
1 PG and Research Department of Computer Science, PresidencyCollege, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 3 (2020), Pagination: 82-92Abstract
Scheduling of tasks in a cloud environment has larger influence on time and energy depletion. Different heuristic models were developed to solve the NP-hard task scheduling problem based on time. However, ideal task scheduling algorithms must also maximize energy efficiency with good load balancing and ensure better Quality-of-Service (QoS). An innovative multi-objective Chaotic Darwinian Chicken Swarm Optimization (CDCSO) system is suggested in this article to provide energy efficient QoS and load balancing aware task scheduling. The multi-objective CDCSO algorithm incorporates the chaotic and Darwinian Theory to the standard Chicken Swarm Optimization to increase its global exploration and maximize the convergence rate. This performance enhanced CDCSO algorithm models the cloud task scheduling problem as NP-hard and utilizes the optimization principles to solve them based on multiple objective parameters. The multi-objective fitness function used in CDCSO is modelled based on the objective parameters namely energy, cost, task completion time, response time, throughput and load balancing index. Based on this multi-objective function, the CDCSO effectively allocates the tasks to the suitable energy efficient, cost and time minimized Virtual machines (VMs) which are also optimally load balanced. CloudSim simulations were conducted and the obtained results illustrated that the proposed multi-objective CDCSO has provided better task scheduling with minimized energy, cost, time and optimal load balancing.Keywords
Cloud Task Scheduling, Multi-Objective Problem, Chaotic Darwinian Chicken Swarm Optimization, Darwinian Theory, Energy Efficiency, Load Balancing Index, Quality-of-Service.Full Text
References
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- Kiruthiga, G. & Mary Vennila, S. (2019). An Enriched Chaotic Quantum Whale Optimization Algorithm Based Job scheduling in Cloud Computing Environment. International Journal of Advanced Trends in Computer Science and Engineering, 6(4),1753-1760.
- Kiruthiga, G. & Mary Vennila, S. (2020). Multi-Objective Task Scheduling using Chaotic Quantum-Behaved Chicken Swarm Optimization (CQCSO) in Cloud Computing Environment.International Conference On Evolutionary Computing And Mobile Sustainable Networks,
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- Raj, B., Ranjan, P., Rizvi, N., Pranav, P., & Paul, S. (2018). Improvised Bat Algorithm for Load Balancing-Based Task Scheduling. In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications (pp. 521-530). Springer, Singapore.
- Xavier, V. A., &Annadurai, S. (2019). Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Cluster Computing, 22(1), 287-297.
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- Robust Resource Scheduling With Optimized Load Balancing Using Grasshopper Behavior Empowered Intuitionistic Fuzzy Clustering in Cloud Paradigm
Abstract Views :272 |
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Authors
Affiliations
1 PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, IN
1 PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 5 (2020), Pagination: 137-145Abstract
With the advancement in internet technology, everyone can able to utilize resources with low cost using cloud resources. There will be numerous requests for task scheduling to share resources in the cloud environment. When the task request is received by the cloud technology it should have the ability to distribute the workload among sharable resources in a balanced manner and effective utilization of resources. Machine learning and metaheuristic algorithms provide a dynamic part in balanced task assignments in the cloud paradigm. Existing unsupervised models-based load balancing, centroid selection is done randomly and imprecise job requests are not well handled by them. This paper aims to develop a clustering model-based task scheduling with the knowledge of behavioural inspired optimization algorithm in a highly balanced manner. A robust Intuitionistic Fuzzy C-means empowered grasshopper optimization has been anticipated in this work, which utilizes the merits of the Intuitionistic fuzzy and Grass Hopper algorithm for prominent task scheduling among virtual servers in a cloud environment. The results proved that IFCM-GOA reduces the makespan, execution time and, high balance load scheduling with improved cloud resource utilization.Keywords
Task Scheduling, Cloud Computing, Machine Learning, Intuitionistic Fuzzy C Means, Grasshopper Optimization.Full Text
References
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- Design and Analysis on Medical Image Classification for Dengue Detection using Artificial Neural Network Classifier
Abstract Views :206 |
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Authors
P. K. Swaraj
1,
G. Kiruthiga
2
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Thirssur, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Computer Science and Engineering, Government College of Engineering, Thirssur, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 3 (2021), Pagination: 2407-2412Abstract
Dengue is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with dengue. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses artificial neural network (ANN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a preprocessing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over dengue image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.Keywords
Machine Learning, Dengue, Classification, Diagnosis.
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- Plant Disease Recognition and Clustering Using Fuzzy Algorithm on Data Mining
Abstract Views :185 |
PDF Views:2
Authors
R. Sabitha
1,
G. Kiruthiga
2
Affiliations
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2429-2432Abstract
Due to large size and intensive processing needs, deep learning models are not suited for mobile and handheld devices. Our goal is to develop a process that begins with pre-processing, diagnoses diseased leaf areas, uses the GLCM to choose and classify features, and culminates in a conclusion. We developed fuzzy decision methods for assigning photos of common rust to various severity levels, using data on diseased leaf regions isolated by threshold segmentation. The outcomes of these experiments were determined by six different colour and texture attributes. In plant disease clustering, the Fuzzy Algorithm is utilised. The test results demonstrate that the new method is more efficient than the conventional approaches and ranks first for feature extraction techniques. This appears to say that plant disease diagnosis using leaves should be utilised. Additional disease classifications or crop/disease classifications can be added to define these capabilities.Keywords
Plant Disease, Plant Leaf, Recognition, Clustering.References
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- Detection Of Liver Cancer From CT Images Using CapsNet
Abstract Views :108 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
3 Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, IN
1 Department of Computer Science and Engineering, Government College of Engineering, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
3 Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2601-2604Abstract
Primary adult liver cancer is currently classified into two main diagnostic categories: cholangiocarcinoma and hepatocellular carcinoma. Hepatocellular carcinoma is the most common kind of liver cancer in adults. These are extremely heterogeneous tumours with a wide range of morphological and clinical characteristics, which reflects the wide range of oncological drugs available and the intricate pathways that lead to carcinogenesis. Because of the large quantity of data acquired from phenotypic and molecular research, the classification of liver cancer is shifting away from the old method, which is based on morphological aspects, and toward a more functional approach based on functional characteristics. In this paper, we develop a Capsule Network (CapsNet) classifier to classify the liver regions from computerized tomography (CT) images. The CapsNet helps in classification of instances using pre-processing and feature extraction stages. The training of the classifier is conducted using various liver images from the input datasets and the classifier is validated using the test images. The simulation is conducted to test the effectiveness of CapsNet and the results of simulation shows that the proposed method achieves higher degree of classification than other methods.Keywords
Medical Images, Deep Learning, CapsNet, Image ProcessingReferences
- S. Karthick, P.A. Rajakumari and R.A. Raja, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
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- V. Maheshwari, M.R. Mahmood and S. Sravanthi, “Nanotechnology-Based Sensitive Biosensors for COVID19 Prediction using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-13, 2021.
- K. Pragash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance between Privacy and Utility using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, pp. 1-16, 2021.
- J.P. Vigueras-Guillen, A. Patra and F. Seeliger, “Parallel Capsule Networks for Classification of White Blood Cells”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 743752, 2021.
- K. Srihari, G. Dhiman, S. Chandragandhi and H.F. Alharbi, “An IoT and Machine Learning‐based Routing Protocol for Reconfigurable Engineering Application”, IET Communications, Vol. 23, pp. 1-19, 2021.
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- A Service Package Identifier Based Security Verification Algorithm For Wireless Mobile AD-HOC Network
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Authors
R. Sabitha
1,
G. Kiruthiga
2
Affiliations
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 1 (2022), Pagination: 2650-2655Abstract
In general, the biggest problem with a mobile ad-hoc network is the threat to its security. This is because the mobile ad-hoc network is dismantled after a certain period of time, which spends a lot of time calculating its stability and greatly wastes its security dimensions. Thus, the security features on these temporary networks need to be strengthened as they pose the most threats. In this paper, a security algorithm designed in SID mode is proposed to fix security vulnerabilities in the wireless mobile ad-hoc network module. Its main feature is that its security definitions are defined according to the number of Service Package Identification assigned to it. The definition of numbers based on its importance is to make a list of related devices in order and, accordingly, bring those devices into the security module. Its security features have been improved so that the security modules remain active as long as the network is active.Keywords
Service Package Identification, Ad-hoc Networks, MANET, Security, StabilityReferences
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- Design of Intrusion Prevention System in Internet of Things Communication
Abstract Views :75 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering Thirssur, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
1 Department of Computer Science and Engineering, Government College of Engineering Thirssur, IN
2 Department of Computer Science and Engineering, IES College of Engineering, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 2 (2022), Pagination: 2718-2722Abstract
In this paper, we model a CAN bus controller for data communication in Internet of Things (IoT) Network. However, most communication taking place via CAN bus may prone to attack. Hence aligning security with Intrusion Prevention System (IPS) to detect and mitigate attacks are required. In this paper, we hence model a IPS system over CAN communication. The model uses logs of communication to get trained and detect the attacks in the network. The simulation is conducted in NS2.34 tool to test the efficacy of the CAN-IoT Model. The results show that the proposed method has higher detection rate than other methods.Keywords
CAN Controller, Internet of Things, Intrusion Prevention System, Detection RateReferences
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