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Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results.

Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts. published

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis.

Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association between objects like people, places, other organizations, products, events, services, and so on. We also propose an associative search model based on the 5WH associated concepts of a crime, i.e. WHAT (what has happened), WHO (who was involved in the crime), WHEN (the temporal information of the crime), WHERE (the geo-spatial information of the crime) HOW (the modus-operandi used in committing a crime). We have employed Formal Concept Analysis theory to reveal the associations, highlighting Hot Spots, offender's profile and its associated offenders in a criminal activity.

Gestational trophoblastic neoplasia (GTN) comprise a spectrum of interrelated conditions originating from the placenta. With sensitive assays for human chorionic gonadotropin (β-hCG) and current approaches to chemotherapy, most women with GTN can be cured with preservation of reproductive potential. The purpose of this analysis was to address the outcome of GTN in patients from a tertiary care center of India.We undertook a retrospective and prospective review of GTN cases treated at our center over a period of 7 years from 2008 to 2014. Patients of GTN were assigned to low-risk or high-risk categories as per the FIGO scoring system. The low-risk group was treated with combination of actinomycin-D and methotrexate and the high-risk group received the Etoposide, Methotrexate, Actinomycin-D/ Cyclophosphamide, Vincristine (EMA/CO) regimen. Salvage therapy was Etoposide, Paclitaxel /Paclitaxel, Cisplatin (EP/TP). Treatment was continued for three cycles after normalization of β-hCG level, after which the patients were followed up regularly.In total, 41 GTN patients were treated at our institution during the above period; 17 were in the low-risk and 24 were in the high-risk category. The lung was the most common site of metastasis. All low-risk patients achieved complete remission. Among high-risk patients, one patient died while receiving first cycle chemotherapy, one patient relapsed, and 22 patients achieved complete remission. The single relapsed patient also achieved remission with second-line chemotherapy.Risk-stratified treatment of GTN was associated with acceptable toxicity and resulted in outcome that was comparable with international standards. The use of two-drug combination in low-risk patients is a better option especially in developing countries.

Traditional on-site wastewater treatment systems have proven to be unsuitable in areas of low permeability subsoils, representing a risk to human health and the environment. With large areas being covered by low permeability tills, Ireland needs to consider alternative treatment and disposal options to be able to allow further development in these areas and to deal with polluting legacy sites. The paper describes the development and structure of a geographic information system (GIS)-based decision support toolset to evaluate possible alternative strategies for these sites. The programme takes as its initial input the location of an existing house located in an area of low permeability subsoils. Through a series of interconnected GIS geoprocesses the model outputs appropriate solutions for a site, ranking them in terms of environmental sustainability and cost. However, the final decisions are still dependent on on-site constraints so that each solution is accompanied by an alert message that provides additional information for the user to refine the output list according to the available local site-specific information.

The work presented in this paper highlights the development of a web based intelligent Geographical Information System (GIS) spatial decision support tool. Engineered over the Amazon cloud computing environment, the GIS system has been designed to enable local authorities in Ireland to identify solutions for the treatment of domestic waste water in areas of low permeability subsoil. The data used in the geo-spatial modelling includes the XY coordinates of existing treatment systems, topography, soils and subsoil, and road networks. Spatial modelling is performed through ESRI's ArcMap and published as a web service on ArcGIS Server 10.1. This decision support system is made available to the remote user over Amazon's EC2 as a GIS based web service, through Representational State Transfer (REST) architecture. The tool will assist local authorities to better manage domestic wastewater in low permeability areas, thereby reducing the likelihood of pollution of local surface waters. Wexford is the test area.