Association Rule Based Classification

In this thesis, we focused on the construction of classification models based on association rules. Although association rules have been predominantly used for data exploration and description, the interest in using them for prediction has rapidly increased in the data mining community. In order to mine only rules that can be used for classification, we modified the well known association rule mining algorithm Apriori to handle user-defined input constraints. We considered constraints that require the presence/absence of particular items, or that limit the number of items, in the antecedents and/or the consequents of the rules. We developed a characterization of those itemsets that will potentially form rules that satisfy the given constraints. This characterization allows us to prune during itemset construction itemsets such that neither they nor any of their supersets will form valid rules. This improves the time performance of itemset construction. Using this characterization, we implemented a classification system based on association rules and compared the performance of several model construction methods, including CBA, and several model deployment modes to make predictions. Although the data mining community has dealt only with the classification of single-valued attributes, there are several domains in which the classification target is set-valued. Hence, we enhanced our classification system with a novel approach to handle the prediction of set-valued class attributes. Since the traditional classification accuracy measure is inappropriate in this context, we developed an evaluation method for set-valued classification based on the E-Measure. Furthermore, we enhanced our algorithm by not relying on the typical support/confidence framework, and instead mining for the best possible rules above a user-defined minimum confidence and within a desired range for the number of rules. This avoids long mining times that might produce large collections of rules with low predictive power. For this purpose, we developed a heuristic function to determine an initial minimum support and then adjusted it using a binary search strategy until a number of rules within the given range was obtained. We implemented all of our techniques described above in WEKA, an open source suite of machine learning algorithms. We used several datasets from the UCI Machine Learning Repository to test and evaluate our techniques

status in gastric carcinoma patients

Effect of chronic smoking on lipid peroxidation and antioxidan

Morin Protects Human Respiratory Cells from PM2.5 Induced Genotoxicity by Mitigating ROS and Reverting Altered miRNA Expression

Chronic fine particulate matter (PM2.5) exposure causes oxidative stress and leads to many diseases in human like respiratory and cardiovascular disorders, and lung cancer. It is known that toxic responses elicited by PM2.5 particles depend on its physical and chemical characteristics that are greatly influenced by the source. Dietary polyphenolic compounds that possess antioxidant and free radical scavenging properties could be used for therapeutic or preventive approaches against air pollution related health hazards. This study evaluates characteristics and toxicity of PM2.5 collected from rural, urban, industrial, and traffic regions in and around Coimbatore City, Tamilnadu, India. Traffic PM2.5 particles contained higher amounts of metals and polycyclic aromatic hydrocarbons (PAHs). It also possessed higher levels of oxidative potential, induced more intracellular reactive oxygen species (ROS), and caused more levels of cell death and DNA damage in human respiratory cells. Its exposure up regulated DNA damage response related miR222, miR210, miR101, miR34a, and miR93 and MycN and suppressed Rad52. Pre-treatment with morin significantly decreased the PM2.5 induced toxicity and conferred protection against PM2.5 induced altered miRNA expression. Results of this study showed that cytoprotective effect of morin is due to its antioxidative and free radical scavenging activity

Conventional radiofrequency ablation of sphenopalatine ganglion for the treatment of cluster headache

Cluster headache is a primary neurovascular unilateral headache associated with autonomic symptoms. The sphenopalatine ganglion plays an important role in the pathogenesis of this disorder. Although medications are the first line of treatment, percutaneous, and surgical interventions have been proposed to treat cluster headache. An attractive option is the radiofrequency ablation of the sphenopalatine ganglion for the treatment of cluster headache due to its relative safety and simplicity compared with other procedures

Overexpression, purification, and pharmacological activity of a biosynthetically derived conopeptide

A high yielding fusion protein system based on the protein cytochrome $b_{5}$ has been used for the production of novel 13-residue acyclic conopeptide. This peptide, Mo1659, can be liberated from the carrier protein using CNBr cleavage and subsequent purification using RP-HPLC methods. The yield of isotopically enriched peptides is high, ranging from 3 to 4 mg of purified peptide from a 500 ml culture, indicating that this system can be widely used for peptide production. Biosynthetic Mo1659 is active on non-inactivating $K^{+}$ channel much like the natural Mo1659, despite the absence of C-terminal amidation. Heteronuclear NMR studies show that the peptide exists in a conformational equilibrium involving proline-10. To our knowledge this is the first report of the production of an isotopically $^{15}N/^{13}C$-enriched conopeptide

Synergetic effect induced/tuned bimetallic nanoparticles (Pt-Ni) anchored graphene as a catalyst for oxygen reduction reaction and scalable SS-314L serpentine flow field proton exchange membrane fuel cells (PEMFCs)

© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A simple design of electroactive and cost-effective electrocatalysts for oxygen reduction reaction (ORR) activity is crucial towards energy conversion in the commercialization of proton exchange membrane fuel cells (PEMFCs). Herein, we synthesized a stable electroactive bimetallic catalyst of Ni anchored with low loading of Pt nanoparticles, and graphene used as a supportive material for catalyst integration (Pt3-Ni/G). It exhibited maximum electrochemical surface area (ECSA, 108.56 m2/gPt), mass activity (2.2 A mgPt) and specific activity (3.47 mA cm-2), signifying an excellent ORR activity. In addition, a scalable PEMFC fabrication through 0.2 mgPtcm-2 Pt3-Ni/G as cathode with an active area of 25 cm2 and stainless steel-314L (SS-314L) used as a serpentine flow field. This strategy provides a maximum power output of 71.25 W mgPt-1 at current density 1.59 A cm-2. In addition, Pt3-Ni/C//Pt/C, based PEMFC system delivered a constant power output (68.75 W mgPt-1) even after 4 h of continuous cycling.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A1030419). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020112382). The authors also wish to acknowledge the support and facilities offered by the PSG management, PSG Institute of Advanced Studies, PSG Sons & Charities, Coimbatore, India.Peer ReviewedPostprint (published version