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Assessing Player Interaction for a Social Networking Cooperative Educational Game
Cooperative interaction in educational games, designed to stimulate teamwork, joint creativity and knowledge sharing, also carries potential security threats. One of the key dangers is data leakage. Player interaction involves the exchange of information, and in case of insufficient protection of the system, confidential data, such as personal information, game progress results or individual strategies, may become available to unauthorized persons. This may result in misuse of information, damage to reputation and violation of player privacy. The impact on the game space is also a threat. By interacting, players can change the game world, for example, by entering incorrect data, moving objects to an inappropriate location, or modifying the rules of the game. This can lead to a violation of the balance of the game, incorrect results and a deterioration in the learning effect. Substitution or falsification of game elements is no less dangerous. Attackers can introduce fake elements into the game space, for example, incorrect reviews, changed rules or incorrect data. This can lead to incorrect conclusions, distort learning outcomes, and undermine confidence in the game. In addition, the use of interaction tools can become an object of attack. Attackers can hack and modify tools, such as communication platforms or data storage systems. This can lead to data theft, incorrect operation of tools and malfunction during the game. It is shown that formal descriptions of the choice of a game strategy can exist in a game. Indicators that are essential for cooperative interaction are determined, and examples of their calculation for the case with remote interaction through a social network are given. The article contains information about collaborations, which can be used to assess and choose the direction of development in projects that use game cooperative strategies to implement tasks other than training. The project highlights aspects of cooperative interaction that affect the formation of game strategies in an educational project. Of particular interest are projects in which a social network is the tool and medium of interaction. The objectives of the project are to identify easy-to-use indicators that show the features of cooperative interaction within an educational game. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Complex Network Articulation Points Detection and Centrality Measures
To clearly understand how network structure and function interact is a basic difficulty in the study of large networked systems. An old-fashioned idea from graph theory, called articulation points, may be used to do this. In a network, a node If removing it causes the network to become disconnected or causes more network components to get linked, it is an articulation point (AP). Single points of collapse are represented as articulation points in networks. The major goal of this research is to provide a method for identifying the articulation points and centrality measures. We can locate the articulation points considerably more quickly and effectively by using TARJANS Algorithm, which uses depth-first search. It must fulfill two requirements to qualify as an articulation point. For the root node of a DFS traversal to be an articulation point, it must contain at least two offspring nodes that are members of various sub graphs. It has been discovered that articulation points (APS) are crucial for maintaining the reliability and connection of several real-world networks. By assigning each node in the graph a scalar value based on an assumption, centrality metrics may be used to quantify each nodes significance. A fundamental centrality metric is node degree. In terms of node neighbors, it is equivalent. Hence, the more neighbors a node has, the more central and densely linked it is, and the more it affects the network by having more neighbors. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025. -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking
The medical sector has advanced in an imposing way, and are coming up with lifesaving models and wearable devices for disease predictions and patient monitoring. The prediction models and wearable devices will lead to immense amount of data collection leading to the dimensionality issues, overfitting and inaccurate results. From the pool of data that we use for our prediction model, we should be able to identify the required information and parameters which gives a positive contribution to the decision making model. Every dataset with higher number of parameters and high dimensionality will tend to the problems of overfitting. Here, we have a dataset of demented and non-demented patients with five conventional features and other physical parameters. Along with these parameters, we are adding three new prediction parameters like glyhb, BMI and Cholesterol, for proving the association of Diabetics and Dementia. After the addition of these parameters, the dataset will have thirty parameters, and dimensionality reduction is done to avoid the condition of overfitting. The work uses Principal Component Analysis(PCA)for reducing the dimensionality, t-SNE for visualization and K means clustering is used to cluster the target variable. The cluster mean of each variable is used to understand the performance of each variable in each cluster. Later, a basic feature ranking method is also implemented which can be further used for the prediction model. The performance metric used in this research work is Silhouette score, Inertia and Inter-Cluster Distance map. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Raman spectrum of graphite layers in Indian coal
Two Indian coals of different rank (bituminous and subbituminous coal) have been demineralized by chemical method. Fourier transform Raman spectroscopy studies have been performed to study the changes in functional groups. Well resolved G peak is observed at 1605 cm-1 and 1590 cm-1 both in bituminous coal and subbituminous coal. With HF leaching, this doublet is reduced to a singlet along with reduction of frequency to 1585 cm -1 in subbituminous coal, where as in bituminous coal the absorption become very distinct. Bituminous coal is showing more intense absorption with HF leaching in this region where as subbituminous coal is shown a reduction in intensity. G' band is observed at ? 2700 cm-1 with almost the same intensity as that of G band. This confirms the presence of multilayer formation of graphite layer. The defect band at 1355 cm-1 is due to benzene or condensed benzene rings present in amorphous carbon. This band is weak in the present study. This is mainly due to immature nature of subbituminous coal than the higher rank bituminous coal. Graphite structure is remained behind after chemical leaching liberated oxygenated functional groups and mineral groups. The decrease of ID/IG ratio indicates that graphitization is increased in bituminous coal. 2011 American Institute of Physics. -
Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer
Dimensionality reduction is an essential feature to reduce the complexity of the computations in the large data set environment. When handling large quantum of medical data set, as in the case like, Lung cancer prediction, based on symptoms and Risk factors, number of attributes/ dimensions pose a major challenge. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are chosen, which are based on statistical, rule based, logic based and artificial Neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Confusion matrix of the models before and after dimensionality reduction is computed. Based on the confusion matrix result the models are compared and based on the performance optimal model is chosen. It is found that Multi-layer perceptron based artificial neural network model gives better performance compared to other approaches. 2012 IEEE. -
Photometric and spectroscopic study of candidate be stars in the magellanic clouds
[No abstract available] -
A Survey of Traditional and Cloud Specific Security Issues
The emerging technology popularly referred to as Cloud computing offers dynamically scalable computing resources on a pay per use basis over the Internet. Companies avail hardware and software resources as service from the cloud service provider as opposed to obtaining physical assets. Cloud computing has the potential for significant cost reduction and increased operating efficiency in computing. To achieve these benefits, however, there are still some challenges to be solved. Security is one of the prime concerns in adopting Cloud computing, since the user's data has to be released from the protection sphere of the data owner to the premises of cloud service provider. As more Cloud based applications keep evolving, the associated security threats are also growing. In this paper an attempt has been made to identify and categorize the security threats applicable to Cloud environment. Threats are classified into Cloud specific security issues and traditional security attacks on various service delivery models of Cloud. The work also briefly discusses the virtualization and authentication related issues in Cloud and tries to consolidate the various security threats in a classified manner. Springer-Verlag Berlin Heidelberg 2013. -
Energy sector in India: Challenges and solutions
Energy plays a vital role in the socio-economic development and human welfare of a country. It is indeed a difficult task to meet the ever increasing demand with minimum environmental risks. Population explosion and economic growth are the two major facts that drives the energy demands. The economic growth rate of India has hit the decade low of 5% in 2012-13, which shows the challenges yet to come. India being a fast developing nation with second largest population in the world, faces a significant challenge to meet the desired economic growth rate and to provide adequate access to affordable and clean energy for a large population. With the growing concern about India's population, energy demands and climatic issues, it is difficult to formulate a sustainable energy plan for the country. At the same time energy plan should have minimal effects on the health of nature by reducing CO2 emissions. To cut down CO2 emissions, to reduce fossil fuel import bills and to reduce the dependence on a third country energy supplies, India has to increase the share of renewable energy sources in the country's final energy consumption to at least 18% by 2020. This paper provides a comprehensive overview of India's energy sector, discusses the current scenario, identifies the energy utilization, challenges and puts forward some effective solutions in meeting the increasing energy demands. 2013 IEEE. -
Growth and characterization of glycine potassium nitrate NLO crystals
Single crystals of glycine potassium nitrate were grown using slow evaporation technique. The solutions were prepared mixing glycine with potassium nitrate in different ratios stirring continuously for an hour to get a saturated solution. It was then kept at room temperature for controlled evaporation. Optically clear and well shaped crystals were obtained and these were characterized by (FTIR) studies, EDAX and X-ray powder diffraction. 2011 American Institute of Physics. -
Structural characterization of paraffin wax soot and carbon black by XRD
From past few decades, an exponential increase in the research related to carbon nanomaterials and their excellent applications has been witnessed. Realizing the need for new potential precursors and cost effective production methods, we have investigated two precursors-paraffin wax soot (CS) and carbon black (CB). Structural and morphological features of the samples are analyzed by various techniques such as X-ray diffraction, high resolution scanning electron microscopy and electron dispersive spectroscopy. The lateral size of the aromatic lamellae, stacking height, the average spacing of the (002) crystallographic planes (d002) and aromaticity are found to be 15.12 44.30 3.57 0.912 and 15.26 43.23 3.68 0.986 respectively for paraffin wax soot and carbon black. Very low ? and ? band intensity ratio shows a low amount of disorder in the samples. SEM micrographs of the samples reveal non-uniform carbon nanospheres of particle sizes 26-94 nm. Asian Journal of Chemistry 2013. -
Revisiting psychotherapeutic practices in Karnataka, India: Lessons from indigenous healing methods
Psychotherapeutic practices in India observes a paradigm shift with the current focus on the indigenous movement which has hit the discipline of Psychology like any other stream in Social Sciences and Humanities. The professional challenges and issues faced by the mental health professionals in this country has always revolved around on the 'uncanny' realm of myths, beliefs and religions as far as mental illness is concerned (Prasadarao & Sudhir, 2001). Efforts have been initiated in exploring the cultural and social roots of the health-illness constructs as well as debating on the possibility of 'integration' of these different philosophies. This paper is designed to understand the various therapeutic forms and processes in indigenous healing practices and to analyse the negotiation between indigenous healing practices and psychotherapy with special reference to Karnataka, one of the States situated in the Southern part of India. The study approaches the cultural landscape of Karnataka state based on a qualitative research design wherein in-depth unstructured interview of healers and mental health practitioners and systematic observation of some indigenous healing forms are adopted as methods of data collection. The paper concludes by looking at the challenges of constructing ethnospecific interventions in psychotherapy and the need to develop more cultural-specific theories taking into account the cultural history of the community. -
Evolutionary algorithm based feature extraction for enhanced recommendations
A major challenge to Collaborative Filtering systems is high dimensional and sparse data which they have to deal with. Feature selection techniques partly address this problem by reducing the feature space and retaining only a representative subset of features. However these techniques do not address the sparsity problem which affects both quality and quantity of recommendations. A more promising direction would be to construct/extract new features which are low dimensional, dense and have more discriminative power. Content based construction of features has been explored in the past. This work proposes a evolutionary algorithm based feature extraction techniques which discover hidden features with high discriminative capacity. Such an approach offers the advantage of discovering features even in the absence of additional information such as item contents etc. The proposed approach is contrasted with content based feature extraction techniques through experiments and the ability of the new approach in discovering interesting and useful features is established. -
An autonomic computing architecture for business applications
Though the vision of autonomic computing (AC) is highly ambitious, an objective analysis of autonomic computing and its growth in the last decade throw more incisive and decisive insights on its birth deformities and growth pains. Predominantly software-based solutions are being preferred to make IT infrastructures and platforms, adaptive and autonomic in their offerings, outputs, and outlooks. However the autonomic journey has not been as promising as originally envisaged by industry leaders and luminaries, and there are several reasons being quoted by professionals and pundits for that gap. Precisely speaking, there is a kind of slackness in articulating its unique characteristics, and the enormous potentials in business and IT acceleration. There are not many real-world applications to popularize the autonomic concept among the development community. Though, some inroads has been made into infrastructure areas like networking, load balancing etc., very few attempts has been exercised in application areas like ERP, SCM, or CRM. In this paper, we would like to dig and dive deeper to extract and explain where the pioneering and path-breaking autonomic computing stands today, and the varied opportunities and possibilities, which insists hot pursuit of the autonomic idea. A simplistic architecture for deployment of autonomic business applications is introduced and a sample implementation in an existing CRM system is described. This should form the basis of new start and ubiquitous application of AC concepts for business applications. 2012 IEEE. -
Mesoporous iron aluminophosphate: An efficient catalyst for one pot synthesis of amides by ester-amide exchange reaction
A series of metal aluminophosphates (MAlP: M = V, Fe, Co, Ni & Cu) were prepared by co-precipitation method. All the materials were characterized by various physico-chemical techniques. The materials were found to be mesoporous and moderately acidic. The catalytic activity of the materials was investigated in the synthesis of benzamides in a single pot reaction under solvent free refluxing conditions from methyl benzoate and different amines. Iron aluminophosphate was found to be the most effective catalyst for the synthesis of benzamides with 100% selectivity. The isolated yield of benzamide varied from 46% to 100% depending on the nature of amine. A possible reaction mechanism has been proposed which correlates the surface acidity and catalytic activity of the catalyst. The catalyst could be recycled for about three times without any appreciable loss in activity, thus making the method ecofriendly and economical. -
Growth and characterization of chalcogenide crystals by vapour method
A horizontal linear gradient two zone furnace was designed and employed to grow single crystals of indium telluride by Physical Vapour Deposition (PVD) method. It was calibrated for various trials including, series and parallel combinations of coils, and set temperatures. Systematic growth runs for chalcogenide crystals were performed by varying the source and growth temperatures. Crystals of different sizes and morphologies were obtained. The morphology and chemical analysis of the grown crystals were investigated by Scanning Electron Microscope (SEM) and Energy Dispersive Analysis using X-rays (EDAX). The hardness of the crystals was estimated using a Vickers microhardness tester. 2011 American Institute of Physics. -
Usage of online educational courses by undergraduate engineering students in Karnataka
Increasing availability of low-cost technology has enabled many students to use online courses to supplement their studies. The emergence of MOOCs (Massively Open Online Courses) has also brought about a great revolution in the teaching and learning methods. In case of Indian students, since most of the online courses available are not customized according to the syllabus, the students do not find them completely useful. In this case, Massively Empowered Classrooms (MEC) provides curriculum based video lectures and quizzes to students free of cost. The students are able to gain a good understanding of the subject and also score well in exams. This paper is based on an exploratory study conducted to analyze the usage of online courses and MEC by the undergraduate engineering students in Karnataka, India. The paper also describes some expectations from students and teachers to improve the reach and impact of online education. 2013 IEEE. -
Occupancy improvement in serviced apartments: Customer profiling
Sustaining and improving higher occupancy and generating steady revenue by bringing the experience of 'Home away from Home'for the Customers is the business model of ServicedApartments Industry. Serviced Apartment Industry has to be highly competitive. Its performance is governed by many factors such as competition, technology, social factors and lastly Customers themselves. This study focuses only on Customer profile. To achieve results, the Serviced Apartment Owners/Managers will need to study Customers' profile and their needs. Customer satisfaction and retention lead to better customer loyalty, occupancy rates, and revenue. In this paper a methodological framework to analyze and profile Serviced Apartment Customers is discussed, focusing on the factors and particularly the Customer information which could help in increasing the Occupancy. There is a trend that would normally go unnoticed if analysis of data is taken at the aggregate level but looking at them individually, it provides interesting information. 2012 Taylor & Francis Group. -
Prominent label identification and multi-label classification for cancer prognosis prediction
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Nae Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction. 2012 IEEE.