Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing

Data Mining is the extraction of valuable information from the patterns of data and turning it into useful knowledge. Data preprocessing is an important step in the data mining process. The quality of the data affects the result and accuracy of the data mining results. Hence, Data preprocessing becomes one of the critical steps in a data mining process. In the research of text mining, document classification is a growing field. Even though we have many existing classifying approaches, Naïve Bayes Classifier is good at classification because of its simplicity and effectiveness. The aim of this paper is to identify the impact of preprocessing the dataset on the performance of a Naïve Bayes Classifier. Naïve Bayes Classifier is suggested as the best method to identify the spam emails. The Impact of preprocessing phase on the performance of the Naïve Bayes classifier is analyzed by comparing the output of both the preprocessed dataset result and non-preprocessed dataset result. The test results show that combining Naïve Bayes classification with the proper data preprocessing can improve the prediction accuracy. In the research of Attributed data mining, a decision tree is an important classification technique. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present the method of improving accuracy for decision tree mining with data preprocessing. We applied the supervised filter discretization on J48 algorithm to construct a decision tree. We compared the results with the J48 without discretization. The results obtained from experiments show that accuracy of J48 after discretization is better than J48 before discretization

The Effect of Emotion on Associative and Item Memory

Numerous studies to date have demonstrated superior memory for emotional compared to neutral stimuli (Kensinger & Corkin, 2004; Bennion et al., 2013). This finding, although relatively stable across the item memory literature, becomes less consistent when examined in tasks measuring memory for associative or source information (Chiu et al., 2013). For this reason, the present study set out to examine how emotional content (negative, positive and neutral word pairs) influences memory in two distinct associative and item recognition tasks: associative identification (AI), associative reinstatement (AR), paired-item recognition, and single-item recognition. In measuring the influence of emotion on associations using an explicit (AI) and implicit (AR) recognition task, our study provides evidence suggesting that the emotion-enhancement (or arousal-dependent amygdala activation) typically observed in the item literature may actually be working against the process of binding (Murray & Kensinger, 2014; Mather, 2007). Additionally, in measuring the influence of emotion in two different item recognition tasks, we also find that presentation of items during encoding and test maybe vital to this effect

Site-directed labelling of proteins for NMR and EPR studies

Site-specific protein labelling presents an important tool for protein structural biology by spectroscopic techniques. This thesis focuses on the development of new spectroscopic labels and labelling strategies to improve the sensitivity, accuracy and scope of NMR and EPR spectroscopy experiments of proteins. Double electron–electron resonance (DEER) spectroscopy measures the distance between two paramagnetic metal centres introduced by site-specific attachment of suitable tag molecules. Good DEER tags possess rigid tethers to position their paramagnetic centres at a well-defined location relative to the protein and deliver narrow DEER distance distributions with high sensitivity, which can provide accurate information about protein flexibility. This thesis introduces new, cyclen-based Gd 3+ tags and small, Gd 3+ chelating tags designed to deliver narrow DEER distance distributions. Chapter 2 describes the development of two cyclen-based double-arm Gd 3+ tags designed for binding to the target protein at two and three points to obtain the narrowest Gd 3+ –Gd 3+ DEER distance distributions ever recorded with proteins. It also describes DEER distance measurements with the iminodiacetic acid tag attached to cysteine (Cys), where tags attached to two neighbouring Cys residues combine to chelate a single Gd 3+ ion. These results have been published in a journal article (Welegedara et al., Chem. Eur. J. 2017, 23, 11694−11702). Chapter 3 discusses two single-armed Gd 3+ tags, a cyclen-based Gd 3+ tag and a PyMTA tag that forms a heptadentate Gd 3+ binding motif. Both tags deliver the shortest possible tethers to cysteine residues and are shown to produce narrow DEER distance distributions in proteins. For applications in NMR spectroscopy, proteins can be labelled site-specifically with NMR probes such as trimethylsilyl (TMS) probes, which deliver readily detectable 1D 1 H-NMR signals. Introduction of paramagnetic tags and NMR probes by attachment to Cys residues requires mutations of native Cys residues to achieve site-selectivity, which is not possible with structurally and functionally important Cys residues. Chapter 4 demonstrates a solution to this limitation by introducing a selenocysteine (Sec) residue, which can be selectively reacted with probe molecules at slightly acidic pH without iiiaffecting naturally occuring Cys residues. To achieve this, a Sec residue was introduced as a photocaged unnatural amino acid (UAA), PSc, to bypass the otherwise unavoidable challenges associated with the natural Sec incorporation mechanism. UV illumination of PSc yielded Sec with no evidence for the formation of undesired dehydroalanine byproducts. Selective tagging of Sec residues with TMS tags was shown to deliver a useful tool for studies of ligand binding to proteins. These results have also been published in a journal article (Welegedara et al., Bioconjugate Chem. 2018, 19, 2257−2264). Site-selective incorporation of isotope-labelled PSc, photolysis and anaerobic deselenization opens an indirect route to labelling a single specific alanine residue in a protein with stable isotopes. Such samples would have important applications in heteronuclear NMR, as they allow the selective detection of the labelled alanine residue with maximal spectral resolution. As shown in Chapter 5, deselenization of selenoproteins into alanine is possible but requires extremely anaerobic conditions to eliminate serine as the main unwanted byproduct. A range of UAAs has been developed to serve as spectroscopic probes or facilitate the introduction of spectroscopic probes via biorthogonal reactions. The increasing demand for proteins with different UAAs and the significant cost of some of these UAAs has led to increasing popularity of cell-free protein synthesis (CFPS) systems, which use amino acids more sparingly than in vivo expression systems. Mutants of pyrrolysyl-tRNA synthetase (PylRS)/tRNA CUA pairs have been developed into a particularly versatile tool for the incorporation of many structurally different UAAs, but most produce disappointedly poor protein yields in vivo. Chapter 6 describes attempts to develop an in- house CFPS system with a PylRS/tRNA CUA pair. In addition, the polyspecific G2 synthetase has been reported to facilitate the incorporation of sterically demanding UAAs and Chapter 7 of this thesis describes attempts to develop a CFPS system with the G2 synthetase

Optimization of Deep Convolutional Neural Network with the Integrated Batch Normalization and Global pooling

Deep convolutional neural networks (DCNN) have made significant progress in a wide range of applications in recent years, which include image identification, audio recognition, and translation of machine information. These tasks assist machine intelligence in a variety of ways. However, because of the large number of parameters, float manipulations and conversion of machine terminal remains difficult. To handle this issue, optimization of convolution in the DCNN is initiated that adjusts the characteristics of the neural network, and the loss of information is minimized with enriched performance. Minimization of convolution function addresses the optimization issues. Initially, batch normalization is completed, and instead of lowering neighborhood values, a full feature map is minimized to a single value using the global pooling approach. Traditional convolution is split into depth and pointwise to decrease the model size and calculations. The optimized convolution-based DCNN's performance is evaluated with the assistance of accuracy and occurrence of error. The optimized DCNN is compared with the existing state-of-the-art techniques, and the optimized DCNN outperforms the existing technique

CURRENT STATUS OF THE ADVANCED LEVEL CHEMISTRY PRACTICAL COMPONENT IN SCHOOLS OF SRI LANKA AND RELEVANT REMEDIAL CHANGES

Chemistry-practical in Advanced Level syllabus is not popular among students though it is a very practical subject which helps to understand the changes of the environment. Enforced demand arises year by year for chemistry as it is a main subject in science stream to enter medical and engineering faculties. In our evaluation system no attention has been given to conduct practical tests. Therefore much value has been given to theory-part than practical in teaching-learning process. As a result, students develop their memorizing power than improving their skills. In the new A/L syllabus there are 45 chemistry practical and every student has to complete at least 80% of the total list to be eligible to the final examination according to the circular. Teachers have much room to give individual attention to the students and can explain the lesson with good understanding of the weaknesses of them in practical classes. Thereby abstract concepts can be converted to concrete concepts very easily. Main objectives of this research are to investigate the participation of A/L students in chemistry practical sessions and to investigate the opinions of A/L students and teachers regarding practical sessions. To achieve this goal, a questionnaire was prepared and distributed among 30 chemistry-teachers in randomly selected five leading schools in Colombo who teach chemistry in both English and Sinhala media. Questionnaire prepared for students were distributed among 200 students who follow the science and mathematics streams of the most leading school in Sri Lanka. It was observed that the basic knowledge of the practical is not given properly before carrying out the practical. Laboratory-facilities also are not up to the standard level. Though teachers’ involvement for practical in every way is very satisfactory, utilizing modern technology and new methodology to teach practical is very poor. Unfortunately more than three fourth of the students do not complete chemistry practical before the examination due to various reasons. In most cases, students don’t get a chance to do the practical themselves and about 98.5% of practical are done by teacher or laboratory assistant. The survey also revealed how the language barrier affects “local-medium” students compared to English-medium students while using modern technology such as internet/ e-mail in searching for additional knowledge. The results of the initial survey clearly explain that there is a major drawback within the whole process of teaching of chemistry practical. The laboratory-facilities even in some of leading schools in Colombo also are not up to the required level. This gives us a hint of the possible situation in rural areas. Therefore these recommendations can be extended to all the schools throughout the country to conduct chemistry practical in A/L syllabus. The request from 70% of students to “Redesign the current teaching method” emphasizes the requirement of immediate solution for this issue.  Article visualizations

Flavonoids and Other Polyphenols Act as Epigenetic Modifiers in Breast Cancer.

Breast cancer is a common cancer that occurs due to different epigenetic alterations and genetic mutations. Various epidemiological studies have demonstrated an inverse correlation between breast cancer incidence and flavonoid intake. The anti-cancer action of flavonoids, a class of polyphenolic compounds that are present in plants, as secondary metabolites has been a major topic of research for many years. Our review analysis demonstrates that flavonoids exhibit anti-cancer activity against breast cancer occurring in different ethnic populations. Breast cancer subtype and menopausal status are the key factors in inducing the flavonoid\u27s anti-cancer action in breast cancer. The dose is another key factor, with research showing that approximately 10 mg/day of isoflavones is required to inhibit breast cancer occurrence. In addition, flavonoids also influence the epigenetic machinery in breast cancer, with research demonstrating that epigallocatechin, genistein, and resveratrol all inhibited DNA methyltransferase and altered chromatin modification in breast cancer. These flavonoids can induce the expression of different tumor suppressor genes that may contribute to decreasing breast cancer progression and metastasis. Additional studies are required to confirm the contribution of epigenetic modifications by flavonoids to breast cancer prevention

Customer Service on Social Media: The Effect of Customer Popularity and Sentiment on Airline Response

Many companies are now providing customer service through social media, helping and engaging their customers on a real-time basis. To study this increasingly popular practice, we examine how major airlines respond to customer comments on Twitter by exploiting a large data set containing Twitter exchanges between customers and three major airlines in North America. We find that these airlines pay significantly more attention to Twitter users with more followers, suggesting that companies literarily discriminate customers based on their social influence. Moreover, our findings suggest that companies in the digital age are increasingly more sensitive to the need to answer both customer complaints and customer compliments while the actual time-to-response depends on customer’s social influence and sentiment as well as the firm’s social media strategy

Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform

The concept of racial inequality has existed from the early days of service provision, with evidence dating back to ancient civilizations. While the emergence of the Internet and social media has drastically transformed almost every aspect of everyday life, including the intrinsic values of social relationships, the impact of racial disparities on receiving services on online platforms is not so evident. Although many consumer brands provide customer service on social media today, little is known regarding the prevalence and magnitude of racial discrimination in the context of social media customer service. Thus, in this study, we examine the existence and the extent of racial discrimination against African-Americans in social media customer service. We analyzed all complaints to seven major U.S. airlines on Twitter for a period of nine months. Interestingly, our empirical analysis finds that African-American customers are less likely to receive brand responses to their complaints on social media. To the best of our knowledge, this is the first study to empirically analyze the racial discrimination phenomenon in the context of social media customer service