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The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

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Big data had accumulated a massive amount of stored data for applications including robotics, internet of things (IoT), and healthcare system. Although the IoT-based healthcare system plays a vital role in big data industry, in some case, the sensing may be difficult to predict the accurate result. The proposed system with artificial intelligence and IoT for Parkinson's disease can enhance the gait performance tremendously. This research clearly defines the role of robots in Parkinson's disease and how they interact with big data analytics. To process the research scheme, data are collected from big data. Moreover, Laser scanned scheme with piecewise linear Gaussian dynamic time warp machine learning is introduced. In order to scan the path for obstacle and safe place, laser scan system is used. The main role of robot is to predict the walker motion and give physical training to the patient. To predict the walker motion of patient, robot has to walk along with patient since the sensors are fixed in both the patient and the robot. Finally, the performance of proposed methodology is evaluated with existing works.

Every year 400 to 450 cases of Rhinosporidium are reported from Trivandrum Medical College. Twenty five swabs were collected from patients suffering from Rhinosporidiosis and cultured in standard media. Positive results were obtained in 23 cases. The conidia produced from the colony were compared with the structures obtained from the patient material. Light microscopy using histopathological techniques were used. The consistant appearance of the organism in patient material, the repeatability of growth in subcultures and the negative growth in controls indicated that the organism grown in cultures is the causative agent of the disease. The effect of parameters like pH, temperatures, etc, were also studied.

This study was conducted to evaluate the phytochemical composition and larvicidal effect of leaf essential oil from Murraya exotica against early fourth-instar larvae of Aedes aegypti, Anopheles stephensi and Culex quinquefasciatus. Gas chromatography (GC) and gas chromatography mass spectrometry (GC-MS) analyses revealed that the essential oil contained 27 components. The major chemical components identified were β-humulene (40.62%), benzyl benzoate (23.96%), β-caryophyllene (7.05%) and α-terpinene (5.66%). The larval mortality was observed after 12 and 24 h of exposure period. The results revealed that essential oil showed varied levels of larvicidal activity against A. aegypti, A. stephensi and C. quinquefasciatus. After 12 h of exposure period, the larvicidal activities were LC₅₀ = 74.7 and LC₉₀ = 152.7 ppm (A. aegypti), LC₅₀ = 56.3 and LC₉₀ = 107.8 ppm (A. stephensi ), and LC₅₀ = 74.4 and LC₉₀ = 136.9 ppm (C. quinquefasciatus) and the larvicidal activities after 24 h of exposure period were LC₅₀ = 35.8 and LC₉₀ = 85.4 ppm (A. aegypti), LC₅₀ = 31.3 and LC₉₀ = 75.1 ppm (A. stephensi), and LC₅₀ = 43.2 and LC₉₀ = 103.2 ppm (C. quinquefasciatus). These results suggest that leaf essential oil from M. exotica is a promising and eco-friendly source of natural larvicidal agent against A. aegypti, A. stephensi and C. quinquefasciatus.

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent-based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.