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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Janet, J.
- Psychosocial Stress Burnout among Professional Managers in Health Service Institutions
Abstract Views :311 |
PDF Views:1
Authors
J. Janet
1,
Velayudhan
1
Affiliations
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
Source
Indian Journal of Health and Wellbeing, Vol 5, No 5 (2014), Pagination: 539–544Abstract
The main objectives of the study are to explore the experiential aspect of Occupational stress Burn out among female and male professional managers in private health service institution, to assess the effect of burn out in the process of management and to intervene psychotherapy for enhancement of the professional management. Quantitative research approach was used in this study. The research design was Pre, Post and follow up experimental research design. The female and male professional managers in the private health service institution were samples of this study. The sample size was 50. Non probability purposive sampling technique used. The instruments used for this study were Maslach burnout inventory (MBI). The intervention therapy used for this study were Jacobson progressive muscle relaxation, systematic desensitization and Counseling. The data was collected by interview method and questionnaires. Statistical analysis was done by repeated measures of ANOVA; post hoc comparison Duncan multiple range test and descriptive statistics. The results of Occupational stress burn out showed a significant difference between the three time periods namely Pre, Post and follow-up among the male and female professional managers in private hospitals.Keywords
Psychosocial Stress, Professional Managers, Health Care Institutions
- Route Maintenance in Dynamic Source Routing Using Link Breakage Prediction Algorithm for Mobile Ad-hoc Networks
Abstract Views :127 |
PDF Views:1
Authors
S. Rajeswari
1,
J. Janet
2
Affiliations
1 St.Peter’s University, Chennai, IN
2 Vel Tech Dr RR & Dr SR Technical University, Chennai, IN
1 St.Peter’s University, Chennai, IN
2 Vel Tech Dr RR & Dr SR Technical University, Chennai, IN
Source
Wireless Communication, Vol 3, No 6 (2011), Pagination: 463-466Abstract
In mobile ad hoc network (MANET), Dynamic Source Routing (DSR) is one of the on demand routing protocols for route discovery and route maintenance. The mobility of the nodes in MANET is very high. Due to this mobility the link will not exist for long time. In this situation it is necessary to find alternative path to make communication between the source and the destination. It is a times consuming process whenever the existing route fails frequently. To overcome this problem we propose a modified DSR by adding link breakage prediction algorithm with existing DSR. When the route is discovered the modified DSR finds two routes. One is primary path and other one is backup path. The link breakage prediction algorithm is used to predict the link breakage time in the communicating route and send the warning message to all neighbors and the source node if the link is soon-to-be-broken. If source receive this message it starts using backup route and if back route also fails then it finds alternative route. The backup route will minimize the time consuming process of finding an alternative route to some extent. The main aim is to reduce the link breakage and routing overhead for MANET using Proactive Route Maintenance (PRM). Adding a link breakage prediction algorithm to the DSR protocol protects the link breakages in MANET and to maintain the route. By using this modified DSR minimum 75% of packet loss is reduced.Keywords
Mobile Ad-Hoc Networks, DSR, Link Breakage, Proactive Route Maintenance.- Improved Energy Efficiency Using Distributed Swarm Intelligence (DSI) in Wireless Mobile Networks
Abstract Views :146 |
PDF Views:4
Authors
Affiliations
1 Department of CSE, Sri Venkateswara College of Engineering & Technology, IN
2 Department of CSE, Dr. M.G.R. Educational & Research Institute University, IN
1 Department of CSE, Sri Venkateswara College of Engineering & Technology, IN
2 Department of CSE, Dr. M.G.R. Educational & Research Institute University, IN
Source
Networking and Communication Engineering, Vol 5, No 1 (2013), Pagination: 1-7Abstract
Distributed Swarm Intelligence (DSI) is a relatively new paradigm being applied in a host of research settings to improve the management and control of large numbers of interacting entities such as communication, computer, mobile networks, satellite constellations and more. Distributed path computation is a core functionality of modern communication networks and is expected to remain so, even though some recent proposals contemplate the use of more centralized solutions and patterns. Depending on the mode of information dissemination and subsequent computation using the disseminated information, there are two broad classes of algorithms: (i) link-state algorithms and (ii) distance-vector algorithms. In both methods, nodes choose successor (next-hop) nodes for each destination based only on local information, along with this objective we are providing an additional algorithm i.e. Mobile Ants-Based Routing (MABR) with DSI to chosen better paths to the destination by effective in an appropriate manner. Using Ant Colony optimization with DSI for the distribution computation is the novel approach carried on which clearly shows the performance improvement of their QoS metrics based on the parameters viz. Delay, Data rate, Packets Received and Packets Lost were analyzed. We are able to achieve consistency across nodes, thus energy Efficiency using Distributed Swarm Intelligence (DSI) in Wireless Mobile Networks.Keywords
MABR-DSI, ACO, Wireless Mobile Networks, Distributive Computing.- Discrete Shearlet Transform based Mass Classification System for Digital Mammograms
Abstract Views :146 |
PDF Views:3
Authors
J. Amjath Ali
1,
J. Janet
2
Affiliations
1 St.Peter’s University, Chennai, Tamil Nadu, IN
2 Dr.M.G.R.University (phase II), Chennai, Tamil Nadu, IN
1 St.Peter’s University, Chennai, Tamil Nadu, IN
2 Dr.M.G.R.University (phase II), Chennai, Tamil Nadu, IN
Source
Digital Image Processing, Vol 5, No 2 (2013), Pagination: 69-73Abstract
In this paper, Discrete Shearlet Transform (DST) basedmass classification system for digital mammogram is developed. Therecent enhancement in multi resolution analysis is DST whichdiminishes the disadvantage of the wavelets that they are not veryeffective if the images containing distributed discontinuities such as edges. Initially, the given mammogram is decomposed by using DSTwith various directions. The features used in depict the masses are theenergies of all directional sub-bands of the decomposed image. In theproposed method 2-level decomposition with 2 to 64 directions areused to extract the features. In the classification stage, Support VectorMachine (SVM) classifier with two levels is proposed. In the first onethe given unknown mammogram is classified into normal or abnormalcategory and finally the abnormal severity is classified into benign ormalignant. Experiments are conducted on Mammography ImageAnalysis society (MIAS) database. The average classificationaccuracy achieved for normal/abnormal is 88.72% andbenign/malignant is 94.74%.
Keywords
Shearlet Transform SVM Classifier, Digital Mammograms, Mass, Benign and Malignant.- Computer Aided Diagnosis using Alarm Pixel Generation and Region Growing Method
Abstract Views :133 |
PDF Views:3
Authors
Affiliations
1 Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
2 Dean of Computing, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
3 Dean & Professor, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
1 Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
2 Dean of Computing, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
3 Dean & Professor, Veltech Dr.RR. & Dr.SR Technical University, Chennai, IN
Source
Digital Image Processing, Vol 4, No 12 (2012), Pagination: 673-677Abstract
Breast cancer is one of the most dangerous diseases that cause innumerable fatal in the female society. Early detection is the only way to reduce the mortality. Due to variety of factors sometimes manual reading of mammogram results in misdiagnosis. So that the diagnosis rate varies from 65% to 85%. Various Computer Aided Detection techniques have been proposed for the past 20 years. Even then the detection rate is still not high. The proposed method consists of the following steps Preprocessing, Segmentation, Feature extraction and Classification. Noise, Artifact and Pectoral region are removed in preprocessing step. Contrast enhancement, alarm region generation and Region growing method is used to segment the mass region. Segmented features are extracted using Gray Level Co-occurrence Matrix. Extracted features are classified using Support Vector Machine. Performance of the proposed system is evaluated using partest method. Proposed algorithm shows 95.2% sensitivity and 94.4% Specificity. The proposed algorithm is fully automatic and will be helpful in assisting the radiologists to detect the malignancy efficiently.Keywords
Mammogram, Computer Aided Detection, Adaptive Histogram, Segmentation, Feature Extraction, Support Vector Machine.- Detection of Cancer Cells in Gabour Filtered Mammogram Using Gray Level Co-Occurrence Matrices
Abstract Views :135 |
PDF Views:2
Authors
S. Meenalosini
1,
J. Janet
1
Affiliations
1 Veltech Dr. RR. & Dr. SR Technical University, Chennai, IN
1 Veltech Dr. RR. & Dr. SR Technical University, Chennai, IN
Source
Digital Image Processing, Vol 3, No 7 (2011), Pagination: 422-427Abstract
This paper presents a hybrid technique which aims to assist radiologist in identifying breast cancer at its earlier stages through mammograms. It is difficult to identify masses in raw mammogram. Hence, in this paper an intelligent system is designed to diagnose breast cancer in mammogram using intelligent techniques such as Gabor filter and gray level co-occurrence matrices. Preprocessing, Segmentation and mass extraction are the three major steps involved in the proposed method. In preprocessing, down sampling and quantization is applied on input mammogram, following it noise removal is efficiently performed using median filter and finally Region of Interest is extracted using histogram matching. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter(off center) in frequency space, termed as-Circular Gaussian Filter (CGF). A CGF can be uniquely characterized by specifying a central frequency and a frequency band. Usually mass appears as a brighter region on a mammogram. Mass region can be segmented out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. Finally extraction of masses is performed using gray level co-occurrence matrices (GLCM) features. GLCM Features like entropy, contrast and homogeneity is analyzed in order to detect whether extracted region contains masses or normal tissue. Efficiency of the proposed method is calculated by analyzing true positive, true negative and false positive, false negative results. Receiver Operating Characteristics curve method is used to analyze efficiency of the proposed method. Thus, the proposed approach would be helpful for automated real time breast cancer diagnosis.Keywords
Gabour Filter, Gaussian Filter, Gray Level Co-Occurrence Matrices, Histogram Matching, Mammogram, Masses, Segmentation.- Performance Evaluation of Wavelet and Contourlet Based Joint Medical Image Compression
Abstract Views :145 |
PDF Views:2
Authors
Divya Mohandass
1,
J. Janet
2
Affiliations
1 St. Peter's University, IN
2 Veltech Dr. R.R. & Dr. S.R. Technical University, IN
1 St. Peter's University, IN
2 Veltech Dr. R.R. & Dr. S.R. Technical University, IN
Source
Digital Image Processing, Vol 3, No 7 (2011), Pagination: 428-431Abstract
Medical images are very crucial in providing a good diagnosis. It becomes imperative for these medical images to be processed. In this paper, we present a lossless image coder based on wavelet transform. The efficiency of wavelet transform in representing smooth edges present in medical images has been proved in literature. It has good localization properties in spatial and frequency domain. Ostu's global thresholding algorithm and Huffman encoding are applied to the wavelet transformed image. This encoding algorithm has been applied to CT, MRI images. The drawback in wavelet when representing edges has been overcome by the contourlet transform. The proposed joint image compression algorithm was applied to the contourlet transformed image. Experimental results indicate a comparative approach of the proposed system between the wavelet and the contourlet transformed image. The results obtained were appreciable in terms of compression ratio and PSNR values.Keywords
Lossless Compression, Global Thresholding, Huffman Encoding, Contourlet, Wavelet.- Knowledge Extraction Technique from Web Documents Using Domain Ontologies for Decision Support
Abstract Views :151 |
PDF Views:2
Authors
Affiliations
1 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai-62, IN
2 Department of CSE, Dr. MGR University, Chennai, IN
3 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai, IN
1 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai-62, IN
2 Department of CSE, Dr. MGR University, Chennai, IN
3 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 16 (2011), Pagination: 970-973Abstract
The Semantic Web aims to extend the existing Web with conceptual metadata that are more accessible to machines and thereby more effectively communicate the proposed meaning of Web resources. We need efficient ways to access and extract knowledge from Web documents in order to make the Semantic Web to life and provide advanced knowledge services. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. This paper links acknowledge-extraction tool with ontology to achieve continuous knowledge support and guide information extraction. The extraction tool is applied onto web documents and extracts knowledge that matches the given Web Query. Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology. It provides this knowledge in an XML format that will be automatically maintained in knowledge base (KB). This knowledge base can be used for further inferences (i.e.) to produce a narrative generation. The user interface is designed in such a manner that it receives the relevant interests of the user and the ontology aware query.Keywords
Ontology, Knowledge Extraction, Web Page Annotations, Semantic Web, Web Search.- Efficient Traffic Free Routing Protocol for Mobile Ad-hoc Networks
Abstract Views :192 |
PDF Views:8
Authors
Affiliations
1 Vel Tech Dr.RR Dr.SR Technical University, Avadi, Chennai, IN
2 Department of CSE, Dr.M.G.R University, Chennai, IN
3 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai-62, IN
1 Vel Tech Dr.RR Dr.SR Technical University, Avadi, Chennai, IN
2 Department of CSE, Dr.M.G.R University, Chennai, IN
3 Department of CSE, Vel Tech Dr.RR & Dr.SR Technical University, Chennai-62, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 13 (2011), Pagination: 878-881Abstract
Mobile Ad-hoc Networks shows unexpected behavior with multiple data streams under heavy traffic load such as multimedia data when it is sent to common destination. The main reason for packet loss in mobile ad hoc networks is due to congestion. In the current design, routing is not congestion-adaptive. Routing may let a congestion happen which is detected by congestion control, but dealing with congestion in this reactive manner results in longer delay and unnecessary packet loss and requires significant overhead if a new route is needed. This problem becomes more visible especially in large-scale transmission of heavy traffic such as multimedia data, where congestion is more probable and the negative impact of packet loss on the service quality is of more significance. In the current design, the routing protocols are not congestion adaptive. The way in which the congestion is handled results in longer delay and more packet loss. When a new route is needed the routing protocols take significant overhead in finding it. In this paper we propose an Efficient congestion Adaptive Routing Protocol for Mobile Ad-hoc Networks, which out performs even during constrained situation. In order to analyze the performance we have chosen popular routing protocols such as AODV and DSR. We strongly argue that routing should not only be aware of but also be adaptive to network congestion.
Keywords
Ad-Hoc Networks, Routing Protocols, Mobile Computing, Congestion Adaptivity.- A Qualitative Study to Assess the Needs and Problems of High School Children with Asthma and Epilepsy
Abstract Views :212 |
PDF Views:0
Authors
J. Janet
1,
K. R. Biju
2
Affiliations
1 SI-MET College of Nursing (State Institute of Medical Education and Technology, Autonomous Society Under Govt. of Kerala), Malampuzha, Palakkad, Kerala-678651, IN
2 EMS Co-operative Hospital and Research Centre, Perintalmanna, Malappuram DT, Kerala ST-679322, IN
1 SI-MET College of Nursing (State Institute of Medical Education and Technology, Autonomous Society Under Govt. of Kerala), Malampuzha, Palakkad, Kerala-678651, IN
2 EMS Co-operative Hospital and Research Centre, Perintalmanna, Malappuram DT, Kerala ST-679322, IN
Source
International Journal of Advances in Nursing Management, Vol 3, No 1 (2015), Pagination: 1-6Abstract
This qualitative study has aimed to explore the needs and problems of high school children with asthma and epilepsy. Generally Children spend most part of their life time in school. Children with specific chronic illness attending schools is interrupted as because of their physical discomfort, treatment for illness and High school children attending schools with chronic illness like asthma and epilepsy is challenging for the ill children, parents, teachers and peer groups. Asthma and Epilepsy has ten percentage prevalence in India and World. The present research study deals with needs and problems of high school children with asthma and epilepsy. Research approach was qualitative approach. Cross sectional study design using qualitative techniques of FGDs (Focus Group Discussion) and In-Depth interviews(ID) was adapted for this study. The study was conducted in Government high Schools and nearby villages of those schools of Coimbatore dist. Tamil Nadu, India. Population of the study were high school children comprised of boys and girls studying in 8th and 9th standard, Government high schools in Coimbatore, Tamil Nadu, India. The data collection process was stopped as reached information redundancy. The data were analyzed by five different stages namely familiarization, identifying a thematic framework, indexing, charting, mapping and interpretation. The study results were explained by main themes derived from FGD with children, parents and in depth interview with teachers and peer group.Keywords
Qualitative Study, Needs and Problems, High School Children, Asthma, Epilepsy.- Multi-Type Classification of Mammogram Abnormalities by GHM and Multiclass SVM
Abstract Views :157 |
PDF Views:3
Authors
Affiliations
1 Department of CSE, Panimalar Institute of Technology, Chennai, IN
2 Sri Krishna College of Engineering and Technology, Coimbatore, IN
1 Department of CSE, Panimalar Institute of Technology, Chennai, IN
2 Sri Krishna College of Engineering and Technology, Coimbatore, IN
Source
Digital Image Processing, Vol 9, No 1 (2017), Pagination: 14-20Abstract
Cancer is a life-threatening disease, which consumes numerous human lives. However, the lifespan of the patients can be extended, when the disease is treated properly at the right time. This article renders a small contribution to the medical world for detecting the abnormalities in the mammograms. The research goal is attained by four important phases such as mammogram pre-processing, segmentation, feature extraction and classification. In the pre-processing phase, median filter is utilized to enhance the quality of an image. The pre-processed image is then segmented by FCM. The features are extracted from the image by Gaussian Hermite Moments, which are proven to be simple, efficient and noise resistant. Finally, multiclass SVM classifies between the normal, malignant and benign kinds of cancer. The performance of the proposed approach is evaluated against several analogous approaches in terms of accuracy, sensitivity and specificity. The proposed approach shows convincing results.
Keywords
CAD System, Breast Cancer Detection, Mammogram Segmentation, Multi-Class SVM, Classification, Abnormality Detection, Gaussian Hermite Moments.- Elm Based Cad System to Classify Mammograms by the Combination of CLBP and Contourlet
Abstract Views :200 |
PDF Views:7
Authors
Affiliations
1 Department of Computer Science Engineering, Panimalar Institute of Technology, IN
2 Sri Krishna College of Engineering and Technology, IN
1 Department of Computer Science Engineering, Panimalar Institute of Technology, IN
2 Sri Krishna College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 4 (2017), Pagination: 1489-1496Abstract
Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP) and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM) is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.Keywords
Breast Cancer, Microcalcification, Mammogram, Classification.References
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- An Efficient and Complete Automatic System for Detecting Lung Module
Abstract Views :197 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-5Abstract
Objectives: To make a fully automated algorithm that is based on simple and quick steps, which produces consistent output for the same inputs. Methods/Statistical Analysis: For thorax and lung segmentation, region growing based method is used to segment the region of interest. The missing parts of the lungs are reconstructed using morphological operations. After that, nodules are detected based on the features of the reconstructed image. Artificial Neural Network has been used for classifying the images. Findings: An aggregate of 100 pictures with determination of 512 × 512 pixels with eight bits for every shading channel are caught. 90% affectability was obtained with 0.05 false positives for each picture. Application/Improvements: This framework distinguishes the phase of lung malignancy. The outcomes demonstrate that the tumors are of various measurements. By estimating the measurements of the tumor the lung disease stage can be recognized precisely utilizing the proposed technique. The outcomes indicate great potential for lung growth identification at beginning time.Full Text
References
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- Integrated Anthropometric Approach for Ceaseless Authentication
Abstract Views :158 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
2 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
3 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 11, No 26 (2018), Pagination: 1-4Abstract
Objectives: To model a novel ceaseless client validation method to authorize the client regardless of their body position before the capturing system. The system ceaselessly validates the client with their various soft anthropometric parameters such as (e.g. wearables and skin) in addition to hard biometrics. Methods/Statistical Analysis: The proposed system mechanically stores in the soft anthropometric parameters each time the client logs in and integrate the anthropometric parametric features along with the conventional face traits for verification thus fusing the combination of hard and soft biometric attributes to attest a client ceaselessly. The methodology comprises of various modes such as initialization, validation and regeneration. Findings: Various samples of facial colour features and user’s cloth colour features are used as soft biometrics in this system for authorization. The experimental results of AR show the extensive improvement over the existing methods. Application/Improvements: This methodology eliminates the challenges faced in face recognition due to different expressions and postures, lighting effects. Thus the key discriminating features are authenticated using hard and soft biometrics thus making it a high secure technology.Full Text
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