Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study

Business Intelligence solutions are key to enable industrial organisations (either manufacturing or construction) to remain competitive in the market. These solutions are achieved through analysis of data which is collected, retrieved and re-used for prediction and classification purposes. However many sources of industrial data are not being fully utilised to improve the business processes of the associated industry. It is generally left to the decision makers or managers within a company to take effective decisions based on the information available throughout product design and manufacture or from the operation of business or production processes. Substantial efforts and energy are required in terms of time and money to identify and exploit the appropriate information that is available from the data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are still limited to a few specific domains. The applications of these techniques in combination with Text Mining methods based on statistical, natural language processing and visualisation techniques could give beneficial results. Text Mining methods mainly deal with document clustering, text summarisation and classification and mainly rely on methods and techniques available in the area of Information Retrieval (IR). These help to uncover the hidden information in text documents at an initial level. This paper investigates applications of Text Mining in terms of Textual Data Mining (TDM) methods which share techniques from IR and data mining. These techniques may be implemented to analyse textual databases in general but they are demonstrated here using examples of Post Project Reviews (PPR) from the construction industry as a case study. The research is focused on finding key single or multiple term phrases for classifying the documents into two classes i.e. good information and bad information documents to help decision makers or project managers to identify key issues discussed in PPRs which can be used as a guide for future project management process

Textual data mining for industrial knowledge management and text classification: a business oriented approach

Textual databases are useful sources of information and knowledge and if these are well utilised then issues related to future project management and product or service quality improvement may be resolved. A large part of corporate information, approximately 80%, is available in textualdata formats. TextClassification techniques are well known for managing on-line sources of digital documents. The identification of key issues discussed within textualdata and their classification into two different classes could help decision makers or knowledge workers to manage their future activities better. This research is relevant for most text based documents and is demonstrated on Post Project Reviews (PPRs) which are valuable source of information and knowledge. The application of textualdatamining techniques for discovering useful knowledge and classifying textualdata into different classes is a relatively new area of research. The research work presented in this paper is focused on the use of hybrid applications of textmining or textualdatamining techniques to classify textualdata into two different classes. The research applies clustering techniques at the first stage and Apriori Association Rule Mining at the second stage. The Apriori Association Rule of Mining is applied to generate Multiple Key Term Phrasal Knowledge Sequences (MKTPKS) which are later used for classification. Additionally, studies were made to improve the classification accuracies of the classifiers i.e. C4.5, K-NN, Naïve Bayes and Support Vector Machines (SVMs). The classification accuracies were measured and the results compared with those of a single term based classification model. The methodology proposed could be used to analyse any free formatted textualdata and in the current research it has been demonstrated on an industrial dataset consisting of Post Project Reviews (PPRs) collected from the construction industry. The data or information available in these reviews is codified in multiple different formats but in the current research scenario only free formatted text documents are examined. Experiments showed that the performance of classifiers improved through adopting the proposed methodology

Textual data mining applications for industrial knowledge management solutions

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Complications In The Management Of High-Energy Closed Fractures Of Proximal Tibial Plateau. A Retrospective Study

Objective: To analyze the management of high-energy Schatzker type V and VI tibial plateau fractures which are associated with infectious and noninfectious complications. Methods: This study was carried out in the Department of Orthopedic Surgery, Rawalpindi Medical University from July 1, 2018, to June 30, 2021. This is a retrospective study which is done in three years. Patients had to be between the ages of 18 and 60, have no history of arthritis, have a closed fracture of the proximal tibia (Schatzker type V and VI), or have AO type 41-C1, C2 or C3 involvement of the lower limb. Each patient received treatment using techniques such as internal fixation with locking plates and open reduction which are minimally invasive. Results: This study involved a total of 132 patients.Mean age was 35.15±10.59.115(87%) were men and 17(13%) were women out of 132. A total of 39 out of 132 patients experienced complications (29.54%). Infectious complications (18.93%) were found in (25/132) patients 16 out of 25 patients had superficial infections. Routine dressing changes and antibiotic treatment were carried out in patients who had superficial infections.9 out of 25 patients who had faced a deep-seated infection underwent repeated implant removal, debridements, amputation, and flap covering depending on the reaction of the host. Noninfectious complications had been reported in 14 patients(10.6%). Six patients had hardware-related issues and four of them required a secondary treatment.08 individuals had malalignment, with five of them having it in their immediate postoperative radiographs and three others having it in their late postoperative radiographs. Conclusion: In closed wounds, substantial soft tissue destruction is linked to the fractures of the proximal tibial plateau, particularly Shatzker type V and VI. By selecting the right patients and minimising soft tissue dissection, the problems related to the management of these fractures can be reduced.

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants

Growth and yield enhancement of carrot through integration of NPK and organic manures

A pot experiment was conducted at Horticulture Experimental Area, Gomal University, Dera Ismail Khan, Pakistan to investigate the combined effects of NPK and organic manures on growth and yield of carrot, for two consecutive years. The experiment was laid out in CRD with six treatments and four replications. Five different organic manures such as poultry manure (PM), sewage sludge (SS), farmyard manure (FYM), press mud (PrM) and goat manure (GM) were applied in combination with NPK, each at recommended levels for two successive years. A fertilizer check (control) was also included as treatment where no fertilizer and manure were used. The study revealed significant improvements in almost all growth and yield attributes by combined application of NPK and organic manures. Among different combinations, NPK + PM surpassed all other treatments by giving maximum leaves per plant (8.73 and 8.13), leaf length (38.17 and 36.77cm), root length (29.30 and 24.83cm), root diameter (3.10 and 3.27cm), root weight per plant (142.40 and 142.00g), total biomass per plant (169.33 and 166.67g) and root yield (56.67 and 56.83 t/ha), during both the experimental years. Similarly, NPK combination with green manure and sewage sludge also produced better results pertaining to carrot growth and production for two consecutive years. It was also observed during the study that control treatment showed poorest findings and placed at lowest levels

Study of Seasonal variation and Index Based Assessment of Water Quality and Pollution in Semi-Arid Region of Morocco

Water resources quality assessment a basic requirement for ensuring its sustainability. Groundwater resources being restricted under the earth crust are at high risk of being polluted as compared to rivers which flow continuously. This study evaluated groundwater quality in Mohammedia prefecture, Morocco in terms of physicochemical parameters and seasonal variation. The physicochemical parameters analysed were Temperature, pH, EC, TDS, Na+, Ca2+, K+, NH4+, NO2-, NO3-, PO43-, SO42. Seasonal variation was evaluated for winter and spring seasons. The water quality was assessed in terms of overall water and Pollution index. Cation/anion ratio to TDS revealed evaporation and rock weathering dominance. Based on Pollution index, water quality of 88% samples was in excellent to good category in winter season. The pollution index during winter season was <1 for all sample locations. In Spring PI was >1 only at Location P1 which was attributed to NO2-. In Spring season 78% water samples were in Good to excellent category. The decrease in concentration during spring season was attributed to lack of soil-water interaction with reduced infiltration rate. The increase in concentration of parameters was attributed to anthropogenic activities. Further studies are needed to establish relationship between infiltration rate and pollutants concentration with respect to precipitation during monsoon season. Even though water quality in majority areas was fit for consumption and domestic use still further analysis should be carried out in terms of heavy metals and other emerging pollutants