Data Mining for Marketing

This paper gives a brief insight about data mining, its process and the various techniques used for it in the field of marketing. Data mining is the process of extracting hidden valuable information from the data in given data sets .In this paper cross industry standard procedure for data mining is explained along with the various techniques used for it. With growing volume of data every day, the need for data mining in marketing is also increasing day by day. It is a powerful technology to help companies focus on the most important information in their data warehouses. Data mining is actually the process of collecting data from different sources and then interpreting it and finally converting it into useful information which helps in increasing the revenue, curtailing costs thereby providing a competitive edge to the organisation

Intelligent Decision Support Systems- A Framework

Information technology applications that support decision-making processes and problem- solving activities have thrived and evolved over the past few decades. This evolution led to many different types of Decision Support System (DSS) including Intelligent Decision Support System (IDSS). IDSS include domain knowledge, modeling, and analysis systems to provide users the capability of intelligent assistance which significantly improves the quality of decision making. IDSS includes knowledge management component which stores and manages a new class of emerging AI tools such as machine learning and case-based reasoning and learning. These tools can extract knowledge from previous data and decisions which give DSS capability to support repetitive, complex real-time decision making.  This paper attempts to assess the role of IDSS in decision making. First, it explores the definitions and understanding of DSS and IDSS. Second, this paper illustrates a framework of IDSS along with various tools and technologies that support it. Keywords: Decision Support Systems, Data Warehouse, ETL, Data Mining, OLAP, Groupware, KDD, IDS

Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

Growing technological progressions have given rise to many issues concerning the contemporary decision making in business, which is a difficult phenomenon without Business Intelligence/ Machine Learning. The linking of machine learning with business intelligence is not only pivotal for business decision making but also for the business intelligence in totality, owing to the reason that in absence of machine learning, decision making couldn’t take place efficaciously. Machines need to learn, re-learn, and then only they can help your learning process. The below paper seeks to make this concept simple/ easy by removing the ambiguities using a general framework. In order to prove the impact of machine learning on business intelligence, we need to forecast the trends, what is going around the world – business has to stay updated, then only it can be a successful endeavour.  The paper posits the basic theories and definitions of business intelligence and machine learning. To learn from the past and forecast the future trends, many companies are adopting business intelligence tools and systems. Companies have understood the brilliance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts and with the help of machine learning. It describes the insights on the role and requirement of real time BI by examining the business needs. Keywords: Business Intelligence (BI); Machine Learning (ML); Artificial Neural Networks (ANN); Self-Organizing Maps (SOM); Data Mining (DM); Data Warehousing (DW)

Dovetailing of Business Intelligence and Knowledge Management: An Integrative Framework

The rapid advancement in Information and Communication Technology is driving a revolutionary change in the way organizations do business. The fast growing capabilities of both generating and collecting data has generated an imperative need for new techniques and tools that can intelligently and automatically transform the processed data into valuable information and knowledge for effective decision making. Business intelligence (BI) plays an important role extracting valuable information and discovering the hidden patterns in internal as well as external sources of data. The main purpose of the BI is to improve the knowledge with information that allows managers to make effective decisions to achieve organizational objectives. However majority of organizational knowledge is in unstructured form or in the minds of its employees. On the other hand, Knowledge Management (KM) encompasses both tacit and explicit knowledge to enhance s the organizations performance by providing collaborative tools to learn, create and share the knowledge within the organization. Therefore, it is imperative for the organizations to integrate BI with KM. The purpose of this paper is to discuss the importance of integration of BI with KM and provide a framework to integrate BI and KM. Keywords: Business Intelligence (BI), Knowledge Management (KM), Scorecard, Dashboard, ETL, Data Mining, OLAP, Tacit Knowledge, Explicit Knowledg

Normalization of gene expression data revisited: the three viewpoints of the transcriptome in human skeletal muscle undergoing load-induced hypertrophy and why they matter

The biological relevance and accuracy of gene expression data depend on the adequacy of data normalization. This is both due to its role in resolving and accounting for technical variation and errors, and its defining role in shaping the view point of biological interpretations. Still, the choice of the normalization method is often not explicitly motivated although this choice may be particularly decisive for conclusions in studies involving pronounced cellular plasticity. In this study, we highlight the consequences of using three fundamentally different modes of normalization for interpreting RNA-seq data from human skeletal muscle undergoing exercise-training induced growth. Briefly, 25 participants conducted 12 weeks of high-load resistance training. Muscle biopsy specimens were sampled from m. vastus lateralis before, after two weeks of training (week 2) and after the intervention (week 12) and were subsequently analysed using RNA-seq. Transcript counts were modelled as (1) per-library-size, (2) per-total-RNA, and (3) per-sample-size (per-mg-tissue). Result: Initially, the three modes of transcript modelling led to the identification of three unique sets of stable genes, which displayed differential expression profiles. Specifically, genes showing stable expression across samples in the per-library-size dataset displayed training-associated increases in per-total-RNA and per-sample-size datasets. These gene sets were then used for normalization of the entire dataset, providing transcript abundance estimates corresponding to each of the three biological viewpoints (i.e., per-library-size, per-total-RNA, and per-sample-size). The different normalization modes led to different conclusions, measured as training-associated changes in transcript expression. Briefly, for 27% and 20% of the transcripts, training was associated with changes in expression in per-total-RNA and per-sample-size scenarios, but not in the per-library-size scenario. At week 2, this led to opposite conclusions for 4% of the transcripts between per-library-size and per-sample-size datasets (↑ vs. ↓, respectively). Conclusion: Scientists should be explicit with their choice of normalization strategies and should interpret the results of gene expression analyses with caution. This is particularly important for data sets involving a limited number of genes or involving growing or differentiating cellular models, where the risk of biased conclusions is pronounced.publishedVersio

Increased biological relevance of transcriptome analyses in human skeletal muscle using a model-specific pipeline

Abstract Background: Human skeletal muscle responds to weight-bearing exercise with signifcant inter-individual diferences. Investigation of transcriptome responses could improve our understanding of this variation. However, this requires bioinformatic pipelines to be established and evaluated in study-specifc contexts. Skeletal muscle subjected to mechanical stress, such as through resistance training (RT), accumulates RNA due to increased ribosomal biogenesis. When a fxed amount of total-RNA is used for RNA-seq library preparations, mRNA counts are thus assessed in diferent amounts of tissue, potentially invalidating subsequent conclusions. The purpose of this study was to establish a bioinformatic pipeline specifc for analysis of RNA-seq data from skeletal muscles, to explore the efects of diferent normalization strategies and to identify genes responding to RT in a volume-dependent manner (moderate vs. low volume). To this end, we analyzed RNA-seq data derived from a twelve-week RT intervention, wherein 25 participants performed both low- and moderate-volume leg RT, allocated to the two legs in a randomized manner. Bilateral muscle biopsies were sampled from m. vastus lateralis before and after the intervention, as well as before and after the ffth training session (Week 2). Result: Bioinformatic tools were selected based on read quality, observed gene counts, methodological variation between paired observations, and correlations between mRNA abundance and protein expression of myosin heavy chain family proteins. Diferent normalization strategies were compared to account for global changes in RNA to tissue ratio. After accounting for the amounts of muscle tissue used in library preparation, global mRNA expression increased by 43–53%. At Week 2, this was accompanied by dose-dependent increases for 21 genes in rested-state muscle, most of which were related to the extracellular matrix. In contrast, at Week 12, no readily explainable dose-dependencies were observed. Instead, traditional normalization and non-normalized models resulted in counterintuitive reverse dose-dependency for many genes. Overall, training led to robust transcriptome changes, with the number of diferentially expressed genes ranging from 603 to 5110, varying with time point and normalization strategy. Conclusion: Optimized selection of bioinformatic tools increases the biological relevance of transcriptome analyses from resistance-trained skeletal muscle. Moreover,normalization procedures need to account for global changes in rRNA and mRNA abundance.publishedVersio

Climatic Changes and Their Effect on Wildlife of District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Climatic changes and their impact are increasingly evident in Pakistan, especially in the mountainous regions. Mountain ecosystems are considered to be sensitive indicators of global warming; even slight variations in temperature can lead to significant shifts in local climate, which can, in turn, drastically affect the natural environment, subsequently altering people’s lifestyle and wildlife habitats. The targeted area for the present research was Lower Dir District, Pakistan. The study gathered the required information from primary and secondary sources. Secondary data on temperature and precipitation were obtained from various sources, i.e., local CBO, including WWF Pakistan. Based on information gathered on climate change and wildlife, a detailed questionnaire was designed. Results showed that no regular pattern of the increase was found in temperature from 2010 to 2018; the same was noticed in the rainfall decrease pattern. Results also showed that the leading causes behind climatic changes are an increase in greenhouse gases due to pollution by industries, vehicles, crushing plants, deforestation, and some natural phenomena such as floods. The study showed that more than 80% of the respondents agreed that climatic effects have a significant impact on wildlife, i.e., the existence of wildlife falls in danger due to climatic changes as it may lead to habitat change, making it difficult for the survival and adaptation of the wildlife. Hence, in consequence, it leads to migration, low growth rate, an increase in morbidity and mortality rate, and finally leading to the extinction of the species or population. It is concluded from the study that people are severely noticing the climatic change and its leading causes are greenhouse gases and deforestation. To control climatic changes and wildlife extinction, we need an appropriate policy for forest conservation, wildlife conservation, prevent hunting, industrial pollution control, vehicle pollution control, increase in plantation, awareness of policy for the control of climatic changes, etc

Analysis of an inflection s-shaped software reliability model considering log-logistic testing-effort and imperfect debugging

Gokhale and Trivedi (1998) have proposed the Log-logistic software reliability growth model that can capture the increasing/decreasing nature of the failure occurrence rate per fault. In this paper, we will first show that a Log-logistic testing-effort function (TEF) can be expressed as a software development/testing-effort expenditure curve. We investigate how to incorporate the Log-logistic TEF into inflection S-shaped software reliability growth models based on non-homogeneous Poisson process (NHPP). The models parameters are estimated by least square estimation (LSE) and maximum likelihood estimation (MLE) methods. The methods of data analysis and comparison criteria are presented. The experimental results from actual data applications show good fit. A comparative analysis to evaluate the effectiveness for the proposed model and other existing models are also performed. Results show that the proposed models can give fairly better predictions. Therefore, the Log-logistic TEF is suitable for incorporating into inflection S-shaped NHPP growth models. In addition, the proposed models are discussed under imperfect debugging environment

Establishment of the Invasive Cactus Moth, \u3ci\u3eCactoblastis cactorum\u3c/i\u3e (Berg) (Lepidoptera: Pyralidae) in Pakistan: A Potential Threat to Cultivated, Ornamental and Wild \u3ci\u3eOpuntia\u3c/i\u3e spp. (Cactaceae)

Subsequent to the significant accomplishment of biological control of Opuntia weeds in Australia, the larvae of the cactus moth, Cactoblastis cactorum (native to parts of South America), were released in many countries for the biological control of native Opuntia species (Simmonds and Bennett, 1966). Inauspiciously, larvae were also released in the Caribbean, where the moth spread naturally and by the human support all over the region (García-Turudi et al., 1971). Its enhanced dissemination rate and the biological potential for invasiveness, suggests that the cactus moth is likely to become an invasive pest of Opuntia in the Southeast United States, Mexico, and southwestern America. Its damage is restricted mainly to the plants of genus Opuntia (plants with the characteristic of flat prickly pear pads of the former genus Platyopuntia, now considered to be the part of the genus Opuntia). In this region, plants of this genus provide valuable resources for humans, livestock, and wildlife such as food, medicine, and emergency fodder, while in the arid and semi-arid regions, the plants play key roles in ecosystem processes and soil conservation. At present, the cactus moth has developed into a severe threat to the high diversity of prickly pear cacti, all over the world for both the native and cultivated species of Opuntia (IAEA, 2002)

Chronic obstructive pulmonary disease does not impair responses to resistance training

publishedVersio