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Rohini, K.
- Socio-demographic Profile of Organophosphorous Poisoning in a Tertiary Care Hospital
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Authors
Affiliations
1 Department of Medicine, JJM Medical College, IN
2 Department of Pathology, S. S. Institute of Medical Sciences and Research Centre, Davangere, Karnataka, IN
1 Department of Medicine, JJM Medical College, IN
2 Department of Pathology, S. S. Institute of Medical Sciences and Research Centre, Davangere, Karnataka, IN
Source
Indian Journal of Public Health Research & Development, Vol 3, No 3 (2012), Pagination: 169-171Abstract
Aim and Objectives: To study the demography, social factors, clinical severity and therapeutic outcome of organophosphorous poisoning.
Design: Descriptive study conducted over a period of six months in emergency medical wards.
Method: Seventy cases of organophosphorous compound poisoning admitted to the emergency department were evaluated in the study.
Results: Forty two (60%) were males, twenty eight (40%) were females. Mean age was 25.82 years. Fifty four (77.1%) cases were attempted suicides and sixteen cases (22.9%) were due to accidental events. Among the suicide attempts, 30 (50.55%) were male patients. The study cases included 34.28% agriculturists and 24.17% coolies.These two groups were in low socio economic status.42 patients (60%) were brought to the hospital with mild symptoms and 11(15.71%) had severe intoxication. 58 (82.85 %) patients had consumed the poison orally. In 12 (17.15%) patients it was accidental via inhalation and dermal absorption. Seven patients with severe intoxication were in an unconscious state at the time of admission and died of respiratory and neurological complication.
Conclusion: Confinement of these harmful, unsafe pesticides away from houses will reduce the easy accessibility of them for impulsive act of suicide or accidental consumption. Personal protective measures have to be undertaken to prevent accidental poisoning by inhalation and absorption. Measures like banning the most toxic organophosphorous poisoning have to be undertaken. Newer biological means of pest control would go a long way in preventing the exposure to the toxic effects of the presently used compounds.
Keywords
Organophosphorous Compound Poisoning, Suicide, Intoxication
References
- Logaraj M. et al .Suicidal attempts reported at a medical college hospital in Tamil Nadu. Indian J Community Med 2005, 30(4) 136-137.
- Govt. of India 2008 Accidental death and suicides in India, New Delhi: National Crime Record Bureau Ministry of Home affairs.
- Suliman MI, Jibran R and Rai M The analysis of Organophosphates poisoning cases treated at Bahawal Victoria Hospital, Bahawalpur in 2000- 2003. Pak J Med Sci 2006, 22(3) 244-249.
- Sureshkumar PN. A descriptive analysis of methods adopted, suicide intent and causes of attempted suicide .Indian J psychol med. 2000, 23(1) 47-55.
- Krupesh N, chandrashekar TR and Ashok AC. Organophosphorous poisoning still a challenging proposition. Indian J Anaesth. 2002; 46(1): 40-43
- Nigam M, Jain AK, Dubey BP, Sharma VK.Trends of organophosphorous poisoning in Bhopal region. An autopsy based study. JIAFM; 2004: 26(2) 63-65.
- Khurana D, Prabhakar s.organophosphorous intoxication. Arch Neurol 2000; 57: 600-602.
- Sahin HA, Sahin I, Arabaci F.sociodemographic factors in organophosphorous poisonings. A Prospective study. Hum Exp Toxicol 2003; 22(7): 349-353.
- Aggregated K Means Clustering and Decision Tree Algorithm for Spirometry Data
Abstract Views :179 |
PDF Views:0
Authors
K. Rohini
1,
G. Suseendran
1
Affiliations
1 Department of Information and Technology, School of Computing Sciences, Vels University, P.V. Vaithiyalingam Road, Pallavaram, Chennai - 600117, Tamil Nadu, IN
1 Department of Information and Technology, School of Computing Sciences, Vels University, P.V. Vaithiyalingam Road, Pallavaram, Chennai - 600117, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 44 (2016), Pagination:Abstract
Objectives: The present research work generally focuses on predicting diseases from the lung disease test by using data mining techniques for spirometry data. Methods/Statistical Analysis: Spirometry is used to create baseline lung function, check out dyspnea, disclose pulmonary disease, watching effects of therapies used to treat respiratory disease, calculate respiratory impairment, evaluate operative risk, and performs surveillance for occupational-relevant lung diseases. Pulmonary function tests are used to find out lung capacity, based on which the many of the lung diseases can be identified. In this research work, a combination of k-means clustering algorithm and Decision tree algorithm was developed. From the results investigation, it is known that the proposed aggregated k-means algorithm and decision tree algorithm for spirometry data is better which compared to other algorithms such as Genetic algorithm, classifier training algorithm, and neural network based classification algorithms. Findings: Existing algorithms are unable to handle noisy data and also with Failure occurrence for a nonlinear data set. It should not classify the data set based on their input attributes. Prediction is not possible for existing system. Applications/Improvement: Spirometry data which is used to predict the lung capacity using Aggregated K-means and Decision tree algorithm. Our proposed approach is evaluated for each dataset accordingly.Keywords
Decision Tree, Pulmonary Function Test Means, Spirometry Data.Full Text
- Predicting Lung Disease Severity Evaluation and Comparison of Hybird Decision Tree Algorithm
Abstract Views :405 |
PDF Views:0
Authors
K. Rohini
1,
G. Suseendran
1
Affiliations
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai, IN
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai, IN
Source
Indian Journal of Innovations and Developments, Vol 6, No 1 (2017), Pagination: 1-10Abstract
Objective: To focus on classification algorithms to arrive better prediction model for Lung Disease Severity.
Methods/Statistical analysis: In therapeutic analyses, the part of information mining methodologies is being expanded. Especially Classification calculations are exceptionally useful in arranging the information, which is critical for basic leadership prepare for therapeutic experts. In this paper the analysis is done in the WEKA apparatus on the spiro informational index.
Findings: The paper embarks to make relative assessment of classifiers, for example, J48, Random forest and proposed Hybird Decision Tree(HDT) Algorithm with regards to Spiro dataset to amplify genuine positive rate and limit false positive rate of defaulters as opposed to accomplishing just higher grouping exactness utilizing WEKA instrument. The tests comes about appeared in this paper are about grouping exactness, affectability and specificity.
Application/Improvements: The outcomes created on this dataset likewise demonstrate that the productivity and exactness of J48 is superior to anything other choice tree classifiers. J48 develops purge branches, it is the most urgent stride for govern era in J48. In more often than not this approach over fits the preparation cases with boisterous information. The proposed Hybird Decision Tree (HDT) Algorithm demonstrates great exactness in less time.
Keywords
Decision Tree, Pulmonary Function Test Means, Spirometry Data, Hybird Decision Tree Algorithm, J48 Algorithm.Full Text
References
- K.Rohini, G. Suseendran. Aggregated k means clustering and decision tree. Indian Journal of Science and Technology.2016; 9(44), 1-6.
- Gareth James, Witten Daniela, Hastie Trevor, Tibshirani Robert. An Introduction to Statistical Learning. New York: Springer. 2015, 315.
- J. Quinlan. Induction of decision trees. Machine Learning. 1986; 1, 81—106.
- J. Quinlan. C4.5: programs for machine learning. San Mateo, CA: Morgan Kaufmann; 1993.
- L. Breiman, J. H. Friedman, R.A. Olshen, C.J. Stone. Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. 1984.
- M.D. Eisner, Nicholas Anthonisen, David Coultas, Nino Kuenzli, Rogelio Perez-Padilla, Dirkje Postma, Isabelle Romieu, E.K. Silverman, J.R. Balmes. Novel risk factors and the global burden of chronic obstructive pulmonary disease(on behalf of the environmental and occupational health assembly committee on nonsmoking COPD) .American Journal Of Respiratory And Critical Care Medicine. 2010; 182, 1-26.
- M.H. Danham, S. Sridhar. Data mining, Introductory and Advanced Topics. Pearson education. 1st edition. 2006.
- WHO - PHE health topics-2016. http:// www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/en/. Date Accessed: 27/09/2016.
- Wenke Lee, S.J. Stolfo, K.W. Mok. A data mining framework for building intrusion detection models. Security and Privacy.1999, Proceedings of the 1999 IEEE Symposium on. 1999.
- Aman Kumar Sharma, Suruchi Sahni. A comparative study of classification algorithms for spam email data analysis. IJCSE. 2011; 3(5), 1890-1895.
- J.R. QUINLAN (munnari! nswitgould.oz! quinlan@ seismo.css.gov) on induction of decision trees, centre for advanced computing sciences. New South Wales Institute of Technology, Sydney. 2007.
- Nishkam Ravi, Nikhil Dadekar, Preetham Mysore, M.L. Littman. Activity recognition from accelerometer data. American Association for Artificial Intelligence, 2005.
- Clemens Lombriser, N.B. Bharatula, Daniel Roggen, Gerhard Tr¨oster. On-body activity recognition in a dynamic sensor network. In BodyNets ’07: Proceedings of the ICST 2nd international conference on Body area networks. ICST, Brussels, Belgium. 2007, 1-6.
- Expedient Intrusion Detection System in MANET Using Robust Dragonfly-Optimized Enhanced Naive Bayes (RDO-ENB)
Abstract Views :47 |
PDF Views:1
Authors
M. Sasikumar
1,
K. Rohini
2
Affiliations
1 School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, IN
2 Department of Information Technology, School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, IN
1 School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, IN
2 Department of Information Technology, School of Computing Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 11, No 1 (2024), Pagination: 46-60Abstract
Mobile Ad hoc networks (MANETs) represent dynamic, self-configuring network environments that provide flexible connectivity but are highly susceptible to security threats. Intrusion detection systems in MANETs need to continuously monitor network traffic for potential intrusions and anomalies. This constant monitoring can be energy-intensive, requiring network nodes to process, analyze, and transmit data. Excessive energy consumption by IDS can deplete node batteries quickly, leading to network disruptions. This research focuses on developing and evaluating an efficient IDS proposed for MANETs called Robust Dragonfly-Optimized Naive Bayes (RDO-ENB). RDO-ENB operates by fusing the simplicity and efficiency of the Enhanced Naive Bayes algorithm with the adaptive capabilities of robust Dragonfly Optimization. This synergy enables RDO-ENB to continuously and dynamically adjust its internal parameters, optimizing its intrusion detection performance in real time. It enhances accuracy and reduces false positives, making it proficient in identifying and mitigating intrusions within the complex and ever-evolving environment of MANETs. The dataset employed for evaluation is NSL-KDD, a widely used dataset for intrusion detection. The results of the IDS implementation demonstrate its proficiency in accurately identifying and mitigating intrusions while minimizing false positives and conserving valuable energy resources.Keywords
Dragonfly, Naive Bayes, Intrusion, MANET, Classification, Chaos.Full Text
References
- B. J. Chang, Y. H. Liang, and Y. M. Lin, “Distributed route repair for increasing reliability and reducing control overhead for multicasting in wireless MANET,” Inf. Sci. (Ny)., vol. 179, no. 11, pp. 1705–1723, 2009, doi: 10.1016/j.ins.2009.01.013.
- Z. A. Younis, A. M. Abdulazeez, S. S. R. M. Zeebaree, R. R. Zebari, and D. Q. Zeebaree, “Mobile Ad Hoc Network in Disaster Area Network Scenario; A Review on Routing Protocols,” Int. J. online Biomed. Eng., vol. 17, no. 3, pp. 49–75, 2021, doi: 10.3991/ijoe.v17i03.16039.
- M. U. Farooq and M. Zeeshan, “Connected dominating set enabled on-demand routing (CDS-OR) for wireless mesh networks,” IEEE Wirel. Commun. Lett., vol. 10, no. 11, pp. 2393–2397, 2021, doi: 10.1109/LWC.2021.3101476.
- R. J. Shimonski, W. Schmied, T. W. Shinder, V. Chang, D. Simonis, and D. Imperatore, “DMZ Router and Switch Security,” in Building DMZs For Enterprise Networks, R. J. Shimonski, W. Schmied, T. W. Shinder, V. Chang, D. Simonis, and D. B. T.-B. Dmz. F. E. N. Imperatore, Eds., Rockland: Syngress, 2003, pp. 369–430. doi: 10.1016/b978-193183688-3/50012-2.
- U. Srilakshmi, S. A. Alghamdi, V. A. Vuyyuru, N. Veeraiah, and Y. Alotaibi, “A Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks,” IEEE Access, vol. 10, pp. 14260–14269, 2022, doi: 10.1109/ACCESS.2022.3144679.
- I. Martins, J. S. Resende, P. R. Sousa, S. Silva, L. Antunes, and J. Gama, “Host-based IDS: A review and open issues of an anomaly detection system in IoT,” Futur. Gener. Comput. Syst., vol. 133, pp. 95–113, 2022, doi: 10.1016/j.future.2022.03.001.
- S. N. G. Aryavalli and H. Kumar, “Top 12 layer-wise security challenges and a secure architectural solution for Internet of Things,” Comput. Electr. Eng., vol. 105, p. 108487, 2023, doi: 10.1016/j.compeleceng.2022.108487.
- A. Chourasia and A. Namdev, “Improved wireless mobile ad hoc network using security schemes against black-hole attack,” Int. J. Emerg. Technol. Adv. Eng., vol. 10, no. 11, pp. 61–69, 2020, doi: 10.46338/ijetae1120_07.
- S. Sargunavathi and J. Martin Leo Manickam, “Enhanced trust based encroachment discovery system for Mobile Ad-hoc networks,” Cluster Comput., vol. 22, pp. 4837–4847, 2019, doi: 10.1007/s10586-018-2405-7.
- S. Gopinath, N. A. Natraj, D. Bhanu, and N. Sureshkumar, “Reliability integrated intrusion detection system for isolating black hole attack in MANET,” J. Sci. Ind. Res. (India)., vol. 79, no. 10, pp. 905–908, 2020, doi: 10.56042/jsir.v79i10.43535.
- N. Rajendran, P. K. Jawahar, and R. Priyadarshini, “Makespan of routing and security in Cross Centric Intrusion Detection System (CCIDS) over black hole attacks and rushing attacks in MANET,” Int. J. Intell. Unmanned Syst., vol. 7, no. 4, pp. 162–176, 2019, doi: 10.1108/IJIUS-03-2019-0021.
- M. Prasad, S. Tripathi, and K. Dahal, “An intelligent intrusion detection and performance reliability evaluation mechanism in mobile ad-hoc networks,” Eng. Appl. Artif. Intell., vol. 119, 2023, doi: 10.1016/j.engappai.2022.105760.
- E. A. Shams, A. Rizaner, and A. H. Ulusoy, “Flow-based intrusion detection system in Vehicular Ad hoc Network using context-aware feature extraction,” Veh. Commun., vol. 41, p. 100585, 2023, doi: 10.1016/j.vehcom.2023.100585.
- R. P. P and S. shankar, “Secure intrusion detection system routing protocol for mobile ad‐hoc network,” Glob. Transitions Proc., vol. 3, no. 2, pp. 399–411, 2022, doi: 10.1016/j.gltp.2021.10.003.
- S. B. Ninu, “An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET,” Eng. Appl. Artif. Intell., vol. 123, p. 105969, 2023, doi: 10.1016/j.engappai.2023.105969.
- M. kumar Pulligilla and C. Vanmathi, “An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection,” Internet of Things (Netherlands), vol. 22, p. 100723, 2023, doi: 10.1016/j.iot.2023.100723.
- N. Omer, A. H. Samak, A. I. Taloba, and R. M. Abd El-Aziz, “A novel optimized probabilistic neural network approach for intrusion detection and categorization,” Alexandria Eng. J., vol. 72, pp. 351–361, 2023, doi: 10.1016/j.aej.2023.03.093.
- A. Mabrouk and A. Naja, “Intrusion detection game for ubiquitous security in vehicular networks: A signaling game based approach,” Comput. Networks, vol. 225, p. 109649, 2023, doi: 10.1016/j.comnet.2023.109649.
- J. L. Webber et al., “An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks,” Comput. Electr. Eng., vol. 111, p. 108964, 2023, doi: 10.1016/j.compeleceng.2023.108964.
- S. Ullah et al., “TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks,” Comput. Networks, vol. 237, p. 110072, 2023, doi: 10.1016/j.comnet.2023.110072.
- M. V. B. M. K. M, C. A. Ananth, and N. Krishnaraj, “Detection of intrusions in clustered vehicle networks using invasive weed optimization using a deep wavelet neural networks,” Meas. Sensors, vol. 28, p. 100807, 2023, doi: 10.1016/j.measen.2023.100807.
- M. V. Rajesh, “Intensive analysis of intrusion detection methodology over Mobile Adhoc Network using machine learning strategies,” Mater. Today Proc., vol. 51, pp. 156–160, 2021, doi: 10.1016/j.matpr.2021.05.066.
- F. S. Al-Anzi, “Design and analysis of intrusion detection systems for wireless mesh networks,” Digit. Commun. Networks, vol. 8, no. 6, pp. 1068–1076, 2022, doi: 10.1016/j.dcan.2022.05.013.
- J. Ramkumar, S. S. Dinakaran, M. Lingaraj, S. Boopalan, and B. Narasimhan, “IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion,” in Lecture Notes in Electrical Engineering, K. Murari, N. Prasad Padhy, and S. Kamalasadan, Eds., Singapore: Springer Nature Singapore, 2023, pp. 17–27. doi: 10.1007/978-981-19-8353-5_2.
- R. Jaganathan, V. Ramasamy, L. Mani, and N. Balakrishnan, “Diligence Eagle Optimization Protocol for Secure Routing (DEOPSR) in Cloud-Based Wireless Sensor Network,” 2022, doi: 10.21203/rs.3.rs-1759040/v1.
- L. Mani, S. Arumugam, and R. Jaganathan, “Performance Enhancement of Wireless Sensor Network Using Feisty Particle Swarm Optimization Protocol,” ACM Int. Conf. Proceeding Ser., pp. 1–5, Dec. 2022, doi: 10.1145/3590837.3590907.
- S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073, 2016, doi: 10.1007/s00521-015-1920-1.