Refine your search
Collections
Co-Authors
Journals
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
Sumithra, S.
- Colon Targeted Drug Delivery System of Phytoconstituents
Abstract Views :427 |
PDF Views:0
Authors
Affiliations
1 Department of Pharmacognosy, Madras Medical College, Chennai, IN
1 Department of Pharmacognosy, Madras Medical College, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 7 (2019), Pagination: 3144-3150Abstract
Now a day’s world health organization encourage, recommends and promotes traditional, herbal remedies in health care sciences, because these drugs low in cost, easily available and are safe. The main advantage of using phytoconstituents is free from adverse effects where none of the medication can do. However, the physiochemical properties such as poor permeation, poor solubility and non- targeting at the active site creates a barriers which hinders its therapeutic efficacy. So, targeted drug delivery strategies are employed to overcome these barriers and provide uniform drug targeting at the active site in desired concentration and improved therapeutic efficacy. These day’s colonic diseases are commonly seen and needs lifelong medical attention. Herbal medicines can play vital role in the treatment of colonic diseases like ulcerative colitis, intestinal bowel syndrome, colon cancer and Crohn’s disease. This review article discusses in brief, introduction to targeted drug delivery, factors influence the drug targeting and approaches for enhancing the therapeutic potential of phytoconstituents having wide biological activities.Keywords
Colon Drug Delivery, Factors, Approach, Phytoconstituents.References
- Anya M. Hillery, Andrew W. Lioyd, James Swarbrick. The Textbook of Drug Delivery and Targeting for Pharmacists and Pharmaceutical Scientists. Special Indian Edition., New Delhi; CRC press: 2010.
- Sardarmal Yadav, Ashish Kumar Pareek, Dr. Shiv Garg, Manoj Kumar and Pradeep Kumar. Recent Advances In Colon Specific Drug Delivery System. World Journal of Pharmaceutical Sciences 2015; 4(6): 1380-1394.
- Nishant Singh and Dr. R.. C. Khanna. Colon Targeted Drug Delivery Systems- A Potential Approach Pharmaceutical Research. The Pharma Inoovation 2012; 1(2): 3120-3132.
- Bhushan Prabhakar Kolte, Kalyani V. Tele, Vinayak S. Mundhe, Sandeep S. Lahoti. Colon Targeted Drug Delivery System- A Novel Perspective. Asian Journal of Biomedical and Pharmacutical Sciences 2012.
- Kushaldhir, Harminderpal Singh Kahlon and Sukhbirkaur. Recent Approaches For Colon Targeted Drug Delivery System. International Journal of Pharmaceutical, Chemical and Biological Sciences 2013; 3(2): 360-371.
- Surender Verma, Vipin Kumar, D. N. Mishra and S. K. Singh. Colon Targeted Drug Delivery: Current And Novel Perspectives. International Journal of Pharmaceutical Sciences and Research 2012; 3(5): 1274-1284.
- Pawar Dhanashree G, Darekar Avinash B, Saudagar Ravindra B. Colon Targeted Drug Delivery System: Pharmaceutical Approaches With Current Trends. World Journal of Pharmacy and Pharmaceutical Sciences 2013; 2(6): 6589-6612.
- Trishna Debnath, Da Hye Kim and Beong Ou Lim. Natural Products as a Source of Antiinflammatory Agents Associated with Inflammatory Bowel Disease. Molecules 2013; 18: 7253-7270.
- Subapriya Rajamanickam and Rajesh Agarwal. Natural Products and Colon Cancer: Currrent Status and Future Prospects. NIH Public Access 2008; 69(7): 460-471.
- Pratik Terse and Rashmi Mallya. Importance of Colon Targeted Drug Delivery System in Herbal Medicines. International Journal of Pharmaceutical Sciences and Research 2017; 8(11): 4513-4524.
- Jasvir Kaurl. Geeta Aggarwal, Anu Maharajan and Satvinder Kaur. Colon Targeted Drug Delivery System To Treat Colorectal cancer. World Journal of Pharmacy and Pharmaceutical Science 2015; 5(01): 407-420.
- Anil Bhandari, Imran Khan Pathan, Peeyush K. Sharma, Rakesh K. Patel, Suresh Purohit. Development and Optimization of Colon Taregeted Drug Delivery System of Ayurvedic Churna Formulation Using Eudragit L100 and Ethly Cellulose As Coating Material. International Journal of Pharmacological and Pharmaceutical Sciences 2013.
- Mundhe Vinayak S, Dodiya Shamsundar S. Review Article: Novel Approach for Colon Targeted Drug Delivery. Indo American Journal of Pharmaceutical Research 2011: 3: 158-173.
- Suresh kumar R, Ganesh GN, Jawahar N, Nagasamy Venkatesh D, Senthil V, Raju l and Samantha M. Formulation and Evaluation of pectin-hydroxy propyl methyl cellulose coated curcumin pellets for colon delivery. Asian Journal of Pharmaceutics 2009; 3(2): 138.
- Danda Sreelatha and Chandan Kumar Brahma. Colon Targeted Delivery – A review on Primary and Novel Approaches. Journal of Global Trends in Pharmaceutical Science 2013; 4(3): 1174-1183.
- Ravi Kumar, M. B. Patil, Sachin R. Patil, Mahesh S. Paschapur. Polysaccharides Based Colon Specific Drug Delivery: A Review. International Journal of Pharm Tech Research 2009; 1(2): 334-346.
- Ahuja Naresh and Bhandari Anil, Devendra Yadav, Atul Garg and Sanjay Khanna. Formulation and Development of Controlled Release Multiparticulate Tabltes Using Blends of Various Natural Gum. International Journal of Research in Pharmacy and Chemistry 2012; 2(3): 2231-2781.
- S. P. Vyas. Theory and Practice in Novel Drug Delivery System. First Edition, India binding house; Noida: 2009.
- Nitin B. Charbe, Paul A. McCarron, Majella E. Lane, Murtaza M. Tambuwala. Application of Three-Dimensional Printing For Colon Targeted Drug Delivery Systems. International Journal of Pharmaceutical Investigation 2017; 7: 47-59.
- Vinay K Gupta, G.Gnanarajan, Preeti Kothiyal. A Review Article on Colonic Targeted Drug Delivery System. The Pharma Innovation 2012; 1(7): 2277-7695.
- Huang Y, Tian R, Hu W, Jia Y, Zhang J, Jiang H and Zhang L: A Novel Plug-Controlled Colon-Specific Pulsatile Capsule With Tablet of Curcumin-Loaded SMEDDS. Carbohydrate Polymers 2013; 92(2): 2218-23.
- R.B. Desi Reddy, K. Malleswari, G. Prasad and G. Pavani. Colon Targeted Drug Delivery System: A Review. International Journal of Pharmaceutical Sciences and Research 2012; 4(1): 42-54.
- Hang M, Viennois E, Prasad M, Zhang Y, Wang L, Zhang Z, Han MK, Xiao B, Xu C, Srinivasan S and Merlin D. Edible Ginger-Derived Nanoparticles: A Novel Therapeutic Approach For The Prevention and Treatment of Inflammatory Bowel Disease and Colitis-Associated Cancer. Biomaterials 2016; 1(01): 321-400.
- Ankit Vajpayee, Suresh Fartyal, Alok Pratap Singh, Sajal Kumar Jha. Formulation and Evaluation of Colon Targeted and Opnion 2011; 1(4): 108-112.
- Madhavi M, Madhavi K and Jithan AV: Preparation and in vitro/in vivo Characterization of Curcumin Microspheres Intended to Treat Colon Cancer. Journal of Pharmacy and Bioallied Science 2012; 4(2): 164.
- Ensemble Miscellaneous Classifiers Based Misbehavior Detection Model for Vehicular Ad-Hoc Network Security
Abstract Views :319 |
PDF Views:1
Authors
S. Sumithra
1,
R. Vadivel
1
Affiliations
1 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, IN
1 Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 8, No 2 (2021), Pagination: 90-107Abstract
Vehicular Ad-Hoc Network is an emerging technology, mainly developed for road safety applications, entertainment applications, and effective traffic conditions. VANET applications work based on the accurate mobility information shared among the vehicles. Sometimes attackers manipulate the mobility information shared by the adjacent vehicle or neighboring vehicle, which results in terrible consequences. To deal with the illusion-based type of attacks, researchers have proposed enormous solutions. Unfortunately, those solutions could not deal with the dynamic vehicle conditions and variable cyber malfunctions, which reduces the misbehavior detection accurateness and increases the false-positive rate. In this paper, the dynamic vehicle context is taken into account to propose a two solutions such as Miscellaneous VANET Classifiers based Misbehavior Detection Model (MVC-MDM) and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection Model (EMVC-MDM). This model is constructed based on the Mobility Data Gathering phase, Mobility Context Feature Extraction phase, Mobility Context Feature Level Fixing phase, Hampel Filter based Context Reference Building phase, Constructing Miscellaneous VANET Classifiers based Misbehavior Detection model and Ensemble Miscellaneous VANET Classifiers based Misbehavior Detection phase. Vehicle context is prepared using the data-centric features and the behavior-based features of the vehicles. The Nonparametric Hampel filter and Kalman filter are used to building the context reference model. These filters discover the temporal and spatial correlation of the uniformity in the current mobility information. Vehicle features are extracted locally according to the stability, likelihood, and performance of the vehicles' mobility information. A random forest based learning algorithm is used to train and test the classifiers. The proposed MVC-MDM and EMVC-MDM has been simulated in various context scenarios and the presence of misbehaving vehicles. NGSIM dataset has been used for extensive simulation. The results prove that the effectiveness and the reliability of the proposed MVC-MDM and EMVC-MDM are higher than the existing misbehavior detection systems.Keywords
Stability, Likelihood, Performance, Hampel Filter, Kalman Filter.References
- Lim, Kiho, Kastuv M. Tuladhar, and Hyunbum Kim. Detecting location spoofing using ADAS sensors in VANETs, in 2019 16th IEEE annual consumer communications & networking conference (CCNC), IEEE, 2019, pp. 1-4.
- H. Vahdat-Nejad, A. Ramazani, T. Mohammadi, and W. Mansoor, A survey on context-aware vehicular network applications, Vehicular Communication, vol. 3, Jan. 2016, pp. 43-57.
- O. A. Wahab, A. Mourad, H. Otrok, and J. Bentahar, CEAP: SVM based intelligent detection model for clustered vehicular ad hoc networks, Expert System Applications, vol. 50, May 2016, pp. 40-54.
- R.W. van der Heijden, S. Dietzel, T. Leinmüller, and F. Kargl, Survey on misbehavior detection in cooperative intelligent transportation systems, IEEE Commun. Surveys Tuts., vol. 21, no. 1, 4th Quart, 2018, pp. 779-811.
- F. Sakiz and S. Sen, A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV, Ad Hoc Network., vol. 61, Jun. 2017, pp. 33-50.
- M. N. Mejri, J. Ben-Othman, and M. Hamdi, Survey on VANET security challenges and possible cryptographic solutions, Vehicular Communication, vol. 1, no. 2, Apr. 2014, pp. 53-66.
- S. Sumithra and R. Vadivel, An overview of various trust models for VANET security establishment, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018, pp. 1-7.
- J. Wang, Y. Shao, Y. Ge, and R. Yu, A survey of vehicle to everything (V2X) testing, Sensors, vol. 19, Jan. 2019, pp. 334.
- R. K. Pearson, Y. Neuvo, J. Astola, and M. Gabbouj, Generalized Hampel filters, EURASIP J. Adv. Signal Process., vol. 2016, pp. 87.
- U. Khan, S. Agrawal, and S. Silakari, A detailed survey on misbehavior node detection techniques in vehicular ad Hoc networks, in Information Systems Design and Intelligent Applications (Advances in Intelligent Systems and Computing), New Delhi, India: Springer, vol. 339, 2015, pp. 11-19.
- F. A. Ghaleb, A. Zainal, A. M. Rassam, and F. Saeed, ``Driving-situation aware adaptive broadcasting rate scheme for vehicular adhoc network, Journal of Intelligent Fuzzy Systems, vol. 35, 2018, pp. 423-438.
- X. Y. Tian, Y. H. Liu, J. Wang, W. W. Deng, and H. Oh, Computational security for context-awareness in vehicular ad-hoc networks, IEEE Access, vol. 4, 2016, pp. 5268-5279.
- S. Dietzel, J. Petit, G. Heijenk, and F. Kargl, Graph-based metrics for insider attack detection in VANET multihop data dissemination protocols, IEEE Transaction on Vehicular Technology, vol. 62, no. 4, May 2013, pp. 1505-1518.
- S. Ruj, M. A. Cavenaghi, Z. Huang, A. Nayak, and I. Stojmenovic, On data-centric misbehavior detection in VANETs, in Proc. IEEE Vehicular Technology. Conf. (VTC Fall), Sep. 2011, pp. 1-5.
- S. Sumithra and R. Vadivel, NB-FTBM model for entity trust evaluation in vehicular ad hoc network security, 2nd International Conference on Ubiquitous Communications and Network Computing, Springer, Cham, 2019, pp. 173-187.
- N. Bissmeyer, W. Michael, and K. Frank, Misbehavior detection and attacker identification in vehicular ad-hoc networks, Tech. Univ. Darmstadt, Darmstadt, Germany, Tech. Rep., 2014.
- A Ghaleb, Fuad, Faisal Saeed, Mohammad Al-Sarem, Bander Ali Saleh Al-rimy, Wadii Boulila, A. E. M. Eljialy, Khalid Aloufi, and Ma1111moun Alazab. Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET, Electronics, vol.9, no. 9, 2020, pp.1411.
- H. Stubing, Car-to-X communication: System architecture and applications, in Multilayered Security and Privacy Protection in Car-to-XNetworks. Wiesbaden, Germany: Springer, 2013, pp. 9-19.
- Zhang, Chunhua, Kangqiang Chen, Xin Zeng, and Xiaoping Xue. "Misbehavior detection based on support vector machine and Dempster-Shafer theory of evidence in VANETs." IEEE Access, vol.6, 2018: 59860-59870.
- S. Sumithra and R. Vadivel, Optimal Innovation-Based Adaptive Estimation Kalman Filter For Measuring Noise Uncertainty During Vehicle Positioning In VANET, International Journal of Applied Mathematics and Computer Science (AMCS), Vol. 31, No. 1, March 2021.
- Kamel, Joseph, Mohammad Raashid Ansari, Jonathan Petit, Arnaud Kaiser, Ines Ben Jemaa, and Pascal Urien. "Simulation framework for misbehavior detection in vehicular networks." IEEE transactions on vehicular technology, vol.69, no. 6, 2020, pp.6631-6643.
- K. Zaidi, M. B. Milojevic, V. Rakocevic, A. Nallanathan, and M. Rajarajan, Host-based intrusion detection for VANETs: A statistical approach to rogue node detection, IEEE Transaction on Vehicular Technology, vol. 65, no. 8, Aug. 2016, pp. 6703-6714.
- Ghaleb, Fuad A., Anazida Zainal, Murad A. Rassam, and Fathey Mohammed, An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications, In 2017 IEEE Conference on Application, Information and Network Security (AINS), IEEE, 2017, pp. 13-18.
- S. A. Soleymani, A. H. Abdullah,W. H. Hassan, M. H. Anisi, S. Goudarzi, and M. A. R. Baee, Trust management in vehicular ad hoc network: A systematic review, EURASIP J. Wireless Communication Network, vol. 1, Dec. 2015, pp. 146.
- Y. Zhang, L. Lazos, and W. Kozma, AMD: Audit-based misbehavior detection in wireless ad hoc networks, IEEE Transaction on Mobile Computing., vol. 15, no. 8, Aug. 2016, pp. 1893-1907.
- Dietzel, Stefan, Rens van der Heijden, Hendrik Decke, and Frank Kargl, A flexible, subjective logic-based framework for misbehavior detection in V2V networks, In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, IEEE, 2014, pp.1-6.
- N. Lyamin, A. Vinel, M. Jonsson, and B. Bellalta, ``Cooperative awareness in VANETs: On ETSI EN 302 637-2 performance,'' IEEE Transaction on Vehicular Technology, vol. 67, no. 1, Jan. 2018, pp. 17-28.
- Sakiz, F.; Sen, S, A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV, Ad Hoc Networks, vol.61, 2017, pp. 33–50.
- Van der Heijden, R.W.; Stefan, D.; Tim, L.; Frank, K, Survey on misbehavior detection in cooperative intelligent transportation systems, IEEE Communications Surveys & Tutorials, vol.21, no.1, 2019, pp.779–811.
- Ho, Yao-Hua, Chun-Han Lin, and Ling-Jyh Chen, On-demand misbehavior detection for vehicular ad hoc network, International Journal of Distributed Sensor Networks, vol.12, no. 10, 2016, 1550147716673928.
- Ghaleb, Fuad A., Mohd Aizaini Maarof, Anazida Zainal, Murad A. Rassam, Faisal Saeed, and Mohammed Alsaedi, Context-aware data-centric misbehavior detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages, Vehicular Communications, vol.20, 2019, 100186.
- Erskine, Samuel Kofi, and Khaled M. Elleithy, Real-time detection of DoS attacks in IEEE 802.11 p using fog computing for a secure intelligent vehicular network, Electronics 8, no. 7, 2019, pp.776.