Survey of machine learning methods for spam e-mail classification
The humongous volume of unsolicited bulk e-mail (spam) which is further increasing, is the major cause for developing anti-spam protection filters. Machine learning provides a very optimized approach to automatically filter spams at a very successful rate. Here, in this paper, we survey some of the most popular machine learning algorithms (Naïve Bayes, k-NN, SVMs and ANN) and their applicability to the problem of spam e-mail classification. Descriptions of the algorithms are presented, and the comparison of their performance on the UCI spam base dataset is presented.
Published by: Sanjana Reddy, Navya Priya N, Varsha R Jenni
Author: Sanjana Reddy
Paper ID: V6I3-1672
Paper Status: published
Published: July 3, 2020
A cross-sectional observational study on adverse drug reactions of anti-depressant drugs and compliance in the psychiatry outpatient department at Tertiary Care Hospital, Mumbai
Background: Adverse drug reaction is now seen as one of the major reasons for mortality and morbidity in the world. Moreover, these ADRs are under-reported and underestimated. Thus, the Pharmacovigilance program has been started to reduce the risk of ADR and the safety of drugs. Depression is the most common disorder affecting people of all ages, sex, socio-economic group and religion all over the world; it may range from a very mild condition, bordering on normality, to severe (psychotic) depression. This study may reveal the common drugs which may induce ADR’s so that preventive care can be taken. Therefore the present study is planned to monitor, detect and analyze the adverse drug reactions of anti-depressant drugs in the Psychiatric Department. Aim and Objectives: Aim-To detects and analyze adverse drug reactions in patients with Depression and study patient’s compliance in tertiary care hospitals. Objectives: Primary objective- To detect the types of ADRs induced by anti-depressant drugs and also estimate its incidence rate. Secondary objective- To assess the causality & probability of ADRs, To assess the severity of patients with respect to ADRs, To study the patient’s compliance towards anti-depressant drugs. Methodology: Approval of Institutional ethics committee was taken prior to the initiation of the study. Enrolment of the patient was done as per inclusion and exclusion criteria. Only follow up patients were considered for our study to observe ADRs with the help of prescription copy. The hospital medical case record form of the patient was studied for the demographics, clinical history, clinical findings, diagnostic results and undergoing treatment and compliance. After total data collection from all patients, the ADRs were analyzed. Result: A total of 200 patients were enrolled in our study. Males were 44% while females were 56%. 49 patients between 31 to 40 years, which was mostly found in this age group. Among all ADRs seen weight gain 4%, insomnia 19%, tremors 9.5%, fatiguability 0.5%, nausea 3.5%, sedation 2%, rash 3.5%, and other 4%. Further, the causality of ADRs was observed respectively by using the WHO-causality assessment scale. In which about 54.5% cases of ADR seen to be possible, while 31.8% were unlikely and 13% of ADR were conditional ADR. Naranjo’s probability scale showed 69.6% of probable ADR while 30.3% of doubtful ADR. 82% of patients adhering to medication was measured by medication adherence rating scale while 18% were not adhering medication properly. Conclusion: Depression was seen mostly among people ranging from 31-40 years of age. Females were most affected by depression than the males. Among all the patients, Insomnia was mostly observed ADR in the patients. Other ADRs seen in patients were tremors, fatiguability, rash, weight gain, etc. Only possible and unlikely ADRs were found. Most of the patients were adhering to medication.
Published by: Dr. Subhangi Parkar, Shamil Darbar, Saurabh Ahire, Bhagyashri Sonavane
Author: Dr. Subhangi Parkar
Paper ID: V6I3-1668
Paper Status: published
Published: July 3, 2020
Development and validation of UV-spectrophotometric methods for simultaneous estimation of chlorzoxazone and tramadol in laboratory mixture
The present manuscript describes a simple, sensitive, rapid, accurate, precise, and economical Simultaneous equation method and first-order derivative spectrophotometry method for the simultaneous determination of Chlorzoxazone and Tramadol in laboratory mixture. The absorbance values at 243.3 nm and 271 nm for the simultaneous equation method and 236.6nm and 213.3nm for the first derivative spectrum were used for the estimation of Chlorzoxazone and Tramadol. This method obeyed beer’s law in the concentration range of 2-10 μg/ml for Chlorzoxazone and 10-100 μg/ml for Tramadol. The solvents used for UV-Spectrophotometric methods was 0.1 N NaOH. The % RSD of accuracy was found to be 0.2086 for Chlorzoxazone and 0.4717 for Tramadol. The method was successfully applied to the laboratory prepared mixture because no interference from the mixture excipients was found. The suitability of this method for the quantitative determination of Chlorzoxazone and Tramadol was proved by validation. The results of analysis have been validated statistically and by recovery studies.
Published by: Rushika Jaiswal
Author: Rushika Jaiswal
Paper ID: V6I3-1678
Paper Status: published
Published: July 3, 2020
Indian Diaspora
It is globally accepted fact that Diasporic writers set a trend since the Independence. Indian Diaspora occupies the second largest place in the world. The population of Diaspora is approximately 25 million, who settled in the whole notable regions of the world. Diasporic literature irrespective of languages, style, literary form and technique attract the readers crossing the borders. It may be stream of consciousness or magic realism or immigrancy of a new land and culture. But nostalgia is all Diaspora writers. It is an Interesting Paradox that a great deal of Indian writing in English is produced not in India but in widely distributed geographical areas.
Published by: Rekha Mahadev
Author: Rekha Mahadev
Paper ID: V6I3-1667
Paper Status: published
Published: July 3, 2020
A review on Bacteriophages: Our saviour and weapon for post-antibiotic world
The rate of Emergence of antibiotic-resistant bacteria has become exponential in recent times due to the irrational exploitation of antibiotics. If this trend continues, the world and human civilization as we know it would be pushed back to the dark pre-antibiotic age. Bacteriophages could hold the key to abate the antibiotic crisis. Bacteriophages are the viruses that specifically infect and kill prokaryotic bacteria sparing human eukaryotic cells and also has a limited effect on the human microbiome this dogma makes them suitable candidates to replace antibiotics in the near future. Bacteriophages, by using a novel mechanism than the conventional antibiotics would empower us to treat drug-resistant bacterial infections, they can also be used along with antibiotics to give a maximum effect to terminate bacterial infections. Phage therapy was already in use before the discovery of antibiotics however, discovery and development of antibiotics dampened their progress in modern therapeutics. In the current scenario, it seems vital to reinvent and reestablish phage therapy and employ them to treat infections by superbugs. Plenty of groundbreaking research has been done in the past few years regarding the usage of phages in modern therapeutics. Bacteriophages, if studied properly, has the potential to supplement or even substitute antibiotics. This review will provide information on the lytic mechanism of phages, summaries of successful research done by using phage therapy against bacteria in the animal model, and current human trials along with synergism between antibiotics and phages. Some of the key aspects of synthetic or bioengineered phages and their uses are also incorporated. Finally, a discussion on solutions for carving a path for the successful employment of phage therapy in the future is inserted.
Published by: Saif Ali Nasir Surti
Author: Saif Ali Nasir Surti
Paper ID: V6I3-1649
Paper Status: published
Published: July 3, 2020
Heuristics and Meta-Heuristics optimization methods in solving Traveling Salesman Problem TSP
In modern societies there are increasingly more often problems of various kinds, and tests are needed to solve them in experimental ways. Although, Develop a mathematical model that closely matches the reality to solve a real life problem is very complicated, since many of these models might has to contain very large number of variables (as a heuristic model that optimizes problems solving results). Furthermore, these shows as difficult problems in controlling subjective behaviours, so They are making it even more complicated than these models resemble reality (wrong solving model leads to a more complex level). The purpose of this research is the study of combinatorial optimization problems using approximate methods. In particular, this work focuses on the analysis of meta-heuristics algorithms based on history and population related to the solution of Travelling Salesman Problem (TSP) like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) and many others, as well as hybrids, which allow efficiently to solve generic problems.
Published by: Jawaher A. Al-Ghamdi, Eyad R Al-Masalmeh
Author: Jawaher A. Al-Ghamdi
Paper ID: V6I3-1427
Paper Status: published
Published: July 2, 2020
