Browsing by Author "Kowsalya, R"

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ANDROID APPLICATIONS FOR LUNG NODULES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
(IGI Global, 2023) Karthikeyan, M P; Banupriya, C V; Kowsalya, R; Jayalakshmi, A
Digital image processing is currently used in various fields of research. One of them is in the field of medicine. In fact, experienced radiologists have difficulty distinguishing the cancerous portions of the blood vessels in the lung or detecting fine nodules that suggest lung cancer on X-ray images. Previous studies have shown that doctors and radiologists fail to detect cancerous patches in 30% of positive cases. Implementation of CAD system to classify and detect parts of cancer has been developed, but the results obtained from this implementation are that there are still many errors in the classification results. Therefore, this study will develop android app image technique to perform the classification process of lung cancer. With this research, it is hoped that the developed algorithm can help doctors and radiologists to detect cancer in a short time with more accuracy. Finally, after 20 iterations, a percentage of 90.65% was attained for the test results' performance in classifying 10 X-ray pictures.
ANDROID APPLICATIONS FOR LUNG NODULES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
(IGI Global, 2023) Karthikeyan, M P; Banupriya, C V; Kowsalya, R; Jayalakshmi, A
Digital image processing is currently used in various fields of research. One of them is in the field of medicine. In fact, experienced radiologists have difficulty distinguishing the cancerous portions of the blood vessels in the lung or detecting fine nodules that suggest lung cancer on X-ray images. Previous studies have shown that doctors and radiologists fail to detect cancerous patches in 30% of positive cases. Implementation of CAD system to classify and detect parts of cancer has been developed, but the results obtained from this implementation are that there are still many errors in the classification results. Therefore, this study will develop android app image technique to perform the classification process of lung cancer. With this research, it is hoped that the developed algorithm can help doctors and radiologists to detect cancer in a short time with more accuracy. Finally, after 20 iterations, a percentage of 90.65% was attained for the test results' performance in classifying 10 X-ray pictures.
CDARGA: CLUSTER-BASED DATA AGGREGATION WITH GENETIC ROUTING ALGORITHM IN WIRELESS SENSOR NETWORKS
(Blue Eyes Intelligence Engineering & Sciences Publication, 2020-03) Kowsalya, R; Rosiline Jeetha, B
In wireless sensor network, devices or nodes are normally battery powered devices. These nodes have imperfect quantity of primary energy that an enthusiastic at dissimilar rates, depending on the power or energy level. A modified centralized cluster based cluster-head selection is a primary issue in existing representative clustering methods such as M-LEACH and SEDC, cluster heads are selected with a optional likelihood in a distributed manner. So, there are huge deviations of the amount of clusters and size for each cluster at each round throughout network lifetime. In order to conquer issues of difficult cluster-head selection and great energy consumption in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), in this paper presents a Modified Centralized Cluster-Head Selection (MCCHS) algorithm based on Simple Energy-efficient Data Collecting (SEDC) protocol was proposed. The proposed system presents a MCCHS algorithm in static manner selection algorithm for cluster head selection technique using NS (Network Simulator) 2.34 Framework. This technique leads to improved CH strength, reduction in the amount of clusters in the network, and improving the energy efficiency.
A DEEP LEARNING MODEL TO PREDICT THE PLACEMENT OF SENSOR IN IOT
(2022) Gandhimathi, K; Banupriya, C V; Devipriya, D; Pandiammal, R; Kowsalya, R; Karthikeyan, M P
A DEEP LEARNING MODEL TO PREDICT THE PLACEMENT OF SENSORS IN IOT
(2022) Kowsalya, R
SECURE AND EFFICIENT FIRE-FLY DATA ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS IN IOT MONITORING SYSTEMS
(IOP Publishing Ltd, 2021-04-23) Kowsalya, R; Rosiline Jeetha, B
In the Electronics world the sensor is used in IoT applications. The sensed data need to be transfer to the appropriate devices as input for further processing. Clustering used to group the sensors which could form cluster and select the nodes head from the cluster. The head of each cluster receives the forwarded data through the cluster member and pass on to nearest permanent fixed station. Identifying cluster head and shortest route identification is a major challenge. This paper proposed a novelty on hybrid decision making algorithm with firefly routing algorithm (HDMFRA) for Cluster Head selection. This research work focusing of three main criteria which could save the energy and extend the life activation of the node, through the usage of energy, amount of nodes adjacent and energy consumption from permanent fixed station. To aggregate the data in optimized manner and to transfer the data in efficient manner Fire Fly routing algorithm was used. Simulation results show that proposed algorithm HDMFRA network in homogeneous environment is effective and prolonging the life time of the node by 25%.
SECURED DATA TRANSMISSION USING PARETO OPTIMIZATION BASED LION SWARM OPTIMIZATION AND DOUBLE ENCRYPTION BASED BLOWFISH ALGORITHM IN WSN
(Association for Computing Machinery, 2023-12) Gripsy Viji, J; Kowsalya, R; Banupriya, C.V; Sathya, R
The protection of wireless sensor networks is a complex challenge due to the inherent characteristics of the sensors themselves. These sensors are characterized by their low memory capacity, constrained energy resources, and lack of early awareness regarding their specific placement within the distribution environment. In order to safeguard the integrity and confidentiality of data during transmission, it is imperative to uphold fundamental security measures. This duty elucidates many methodologies for safeguarding data transmission. The primary objective of this research endeavor is to ensure the security of the Wireless Sensor Network (WSN). Several studies and approaches have been proposed; nonetheless, the comprehensive understanding of time and safety remains largely unexplored. The current methodologies exhibit limitations in terms of temporal efficiency and the security of Wireless Sensor Networks (WSNs). In order to address the aforementioned concerns, this research study proposes the utilization of the Pareto Optimization Based Lion Swarm Optimization and Double Encryption based Blowfish method (PLSO-DEBF) method as a means to enhance overall system performance. The primary contributions of this study encompass the development of a comprehensive system model, the utilization of PLSO-DEBF for the selection of transmission nodes, and the incorporation of secure data transmission inside the system model. By leveraging the efficient algorithms of Wireless Sensor Networks (WSNs), this approach demonstrates enhanced performance.