Competency Questions(CQs) play an important role in the ontology development lifecycle, as they represent the ontology requirements. Although the main methodologies describe and use CQs, the current practice of ontology engineering makes a superficial use of CQs. One of the main problems that hamper their proper use lies on the lack of tools that assist users to check if CQs are being fulfilled by the ontology being defined, particularly when these ontologies are defined in OWL (Ontology Web Language), under the Description Logic formalism. We propose a mechanism to support evaluating whether the ontology follows their correspondent CQs.

Evaluating Ontologies with Competency Questions.

The psychology of crime. i

The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion‐based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.

Crime activities prediction system in video surveillance by an optimized deep learning framework

Diabetes Mellitus is commonly found in human beings around the world and this is one of the serious diseases which causes boundless suffering among patients. There are numerous reasons for the prevalence of this disease. It would be better to consider the predictions carried out earlier in this respect, since diabetes is a non - communicable disease and makes a great impact on the health condition of people nowadays. This is the reason why the existing medicinal practices in most hospitals are collecting patients' life history or the record of the disease. This is done for diagnosing diabetes using various medical tests followed by proper treatment for the disease. Machine learning provides an immense contribution to the sector of healthcare. For this research, Pima Indians Diabetes Dataset, obtained from the University of California, Irvine (UCI) machine learning source that included 768 patients' details along with nine attributes had been chosen for a comprehensive investigation of this grave and widespread problem in the health sector. Eventually, an adequate perfect outcome could be achieved and some effective and transparent conclusions were made. Among 768 diabetics, 500 were recognized as positive for the disease while 268 were recognized as negative. Besides, the recorded facts were put into particular supervised machine learning techniques such as Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA), Logistic Regression (LR) and k-nearest neighbors (k-NN). Along with this, bagging and boosting techniques like Random Forest (RF), Extreme Gradient Boosting (XGBoost), LightGBM, and CatBoost too were taken into consideration. In addition, by considering classifiers with the highest accuracies, the final ensemble model was developed with the adaption of SVM, CatBoost and RF to predict the diabetes mellitus. Thus, the model resulted in an accuracy of 86.15%.

Hyper Parameter Tuned Ensemble Approach for Gestational Diabetes Prediction

Logical concept mapping and social media analytics relating to cyber criminal activities for ontology creation

Handwritten character recognition is widely used for the English language. It is difficult to create a character recognition model for south Asian languages because of its shape and compound characters. Among other South Asian languages (e.g.: - Tamil, Hindi, Malayalam, etc.) Sinhala characters are unique, because of their shape, which are having mostly curves and dots. These unique characteristics make it difficult to create a model to recognize Sinhala's handwritten characters. Recognizing handwritten characters rather than typed characters is more complicated because the handwriting of each individual is varying from each other. Therefore the recognition of Sinhala handwritten character need to be improved. Convolutional Neural Network (CNN) is playing a vital role in character recognition by supporting the more efficient image classification. This research focuses on recognizing Sinhala handwritten characters using CNN. Google colaboratory platform is used for the experiment, and python programming language is used for the implementation part. In total, around 110,000 image data were used for the experiment. CNN's performance was evaluated by training and testing the dataset by increasing the number of character classes. When it reaches 100 character class it shows reasonable accuracy of 90.27%. The model was trained by 5 sets of different 100 character classes. Finally, the overall accuracy of 82.33% is achieved for 434 characters. This model outerformed than similar systems.

Sinhala Handwritten Character Recognition using Convolutional Neural Network

Deep Neural Network-Based Crime Prediction Using Twitter Data

Maintaining a customer base at a feasible rate is considered important in most business organizations since customers are the precious asset of the business sector. It is a vital task to retain the customers at a steady level in any business enterprise for the overall stability of its business activities. Sufficient pieces of evidence in this connection have been gathered to prove that the telecommunication industry is the most affected field of business by the tendency of the customers to shift towards alternative service providers. Therefore, a distinctive effort has been made to design this specific forecasting method is carried out utilizing a combination of a properly approachable method aiming at clarifying the probability of the above-mentioned tendency of the clients seeking an alternative service provider in the industry. In this attempt, a data set that included 10, 000 postpaid consumer particulars including 20 attributes were taken for this research for a thorough analysis of this aggravating issue in the telecommunication industry. In the end, a satisfying outcome was witnessed and certain clarification was made out of the 10,000 subscribers 4888 showed positive attitudes and 5112 indicated negative to the churning behavior. Besides, this specific data set was subjected to complete verification in comparison to certain supervised machine learning algorithms such as Decision tree, Logistic Regression, Support Vector Machine (SVM), and Artificial Neural Networks (ANN). Along with this, ensemble techniques such as Random Forest, Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) also have been considered. Subsequently, an assurance was made that XGBoost possessed the ability to bring out the maximum and precised accuracy of 82.90%. Eventually, a hyperparameter tuning had been performed with XGBoost. As a result, an assurance was acquired that XGBoost showed an upsurge in the previously obtained accuracy from 82.90% to 83.13%.

Development of Churn Prediction Model using XGBoost - Telecommunication Industry in Sri Lanka

In times of natural disasters such as floods, tsunamis, earthquakes, landslides, etc., people need information so that relief operations such as help can save many lives. The implications of using social media in post-disaster management are explored in the article. The approach has three main parts: (1) extraction, (2) classification, and (3) validation. The results prove that machine learning algorithms are highly reliable in elimination disaster non-related tweets and news posts. The authors strongly believe that their model is more reliable as they are validating the tweets using news posts by providing various ratings according to the trueness.

Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

Crime is defined as an act harmful not only to the individual involved but also to the community as a whole. We can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. Recently, Social Networks are becoming a great environment for sharing news. Thus, we can use social media like twitter to detect crimes related activities. Because Twitter users sometimes convey messages related to his or her surrounding environment via twitter. In this paper, we proposed a machine learning approach to detect the crimes and the location of the crimes. As the first step, we fetch the twitter posts using predefined keywords relating to the crimes. Then, we constructed an Artificial Neural Network (ANN) model to classify the twitter posts. Then in the final stage, we get the geolocation and crime type. The empirical study of our prototyping system has proved the effectiveness of our proposed crime detection approach.

Banujan Kuhaneswaran

Papers

Sinhala Handwritten Character Recognition using Convolutional Neural Network

Deep Neural Network-Based Crime Prediction Using Twitter Data

Development of Churn Prediction Model using XGBoost - Telecommunication Industry in Sri Lanka

Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

Detecting Crime Related Twitter Posts using Artificial Neural Networks based Approach