Informatics Publishing Limited

D. Loganathan ¹, S. Kamatchiammal ², R. Ramanibai ¹, D. Jayakar Santhosh ², V. Saroja ², S. Indumathi ²

Affiliations
1 University of Madras, Department of Zoology, Maraimalai Campus, Chennai-600 025, IN
2 National Environmental Engineering Research Institute, Chennai Zonal Laboratory, Tamilnadu-600 113, IN

Abstract

Chennai formerly known as Madras, is the capital of the state of Tamil Nadu and India's fourth largest metropolitan city. The status of the groundwater depends on a large number of individual hydro-biological parameters. Pollutants are added to the groundwater system through anthropogenic activities and natural processes. Solid waste from industries is being dumped near the factories and subjected to reaction with percolating rainwater and reaches the groundwater level. The percolating water picks up a large amount of dissolved constituents and reaches the aquifer system and thus it contaminates the groundwater. The aim of the present study was carried out to assess the status of the groundwater in Chennai city using physicochemical and biological parameters according to the standard methods (APHA 1998). Two zones (North and South) from Chennai city were selected for the studies from each Zone 25 sampling stations were fixed and the analysis was made during summer and monsoon seasons (Jan - Dec) 2007. Results indicate that the groundwater of the study area is bacteriologically not safe and need treatment before it is used for drinking purposes. Thus, this study assumes greater importance in the public health management point of view.

Keywords

Groundwater, Chennai, Pollution, Public Health, India

Full Text

   

C. Bhuvaneswari ¹, P. Aruna ¹, D. Loganathan ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN

Abstract

Lung diseases are one of the most common diseases that affect the human community worldwide. When the diseases are not diagnosed they may lead to serious problems and may even lead to transience. As an outcome to assist the medical community this study helps in detecting some of the lung diseases specifically bronchitis, pneumonia and normal lung images. In this paper, to detect the lung diseases feature extraction is done by the proposed shape based methods, feature selection through the genetics algorithm and the images are classified by the classifier such as MLP-NN, KNN, Bayes Net classifiers and their performances are listed and compared. The shape features are extracted and selected from the input CT images using the image processing techniques and fed to the classifier for categorization. A total of 300 lung CT images were used, out of which 240 are used for training and 60 images were used for testing. Experimental results show that MLP-NN has an accuracy of 86.75 % KNN Classifier has an accuracy of 85.2 % and Bayes net has an accuracy of 83.4% of classification accuracy. The sensitivity, specificity, F-measures, PPV values for the various classifiers are also computed. This concludes that the MLP-NN outperforms all other classifiers.

Keywords

Feature Extraction, Multilayer Perceptron, Neural Networks, Bayes Net, Sensitivity, Specificity, F-Measure.

Full Text

     

M.J. Sridevi ¹, Pradosh Kumar Sharma ², M. Dhiliphan Kumar ³, D. Loganathan ², Saleem Ahmed ⁴

Affiliations
1 Department of Computer Science, Government First Grade College for Women, Hassan, Karnataka, IN
2 Department of Information Science and Engineering, Cambridge Institute of Technology, IN
3 Department of Master of Business Administration, Kalasalingam Academy of Research and Education, IN
4 Department of Computer Science, Oryx Universal College, QA

Abstract

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency.

Keywords

5G Networks, Dynamic Resource Allocation, Fractional Pilots, Interference Management, Spectral Efficiency.

Full Text