Stock price prediction has become a critical area of interest for investors and market analysts, though forecasting stock market trends remains a challenging endeavor due to the inherent volatility and unpredictability of the market. The process of stock price prediction typically involves estimating future prices based on historical data, market trends, and various socioeconomic factors. However, factors like market fluctuations, incomplete or erroneous data, and investor behavior add complexity to these predictions. Several methods are employed for stock price forecasting, including fundamental analysis, technical analysis, and machine learning approaches such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. This study focuses on using sectoral indices as benchmarking tools to evaluate sector performance. Specifically, it explores the co-movements of thirteen NSE sectoral indices, with one index chosen as the target. The analysis centers on using closing prices to measure sector performance and calculates the correlations between the target index and others. The six most highly correlated indices are identified, and association rule mining is used to uncover the relationships between these indices and the target index. The study aims to: (i) examine the interdependencies between the target sector and other sectors, and (ii) generate predictive rules for a sector’s performance based on the behavior of correlated sectors, providing valuable insights for making informed investment decisions.

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets.

Dengue fever is mostly prevalent in tropical and subtropical regions, where Aedes mosquitoes, the primary vectors for the virus, thrive in warm and humid environments. In West Bengal, a province in India, the typical duration of the dengue disease spans two to three months, necessitating substantial infrastructure for dengue patients during this period. If the government heavily invests in developing this infrastructure, there's a risk of these facilities remaining underutilized during periods of low dengue incidence. Conversely, without adequate investment, dengue could potentially escalate into an epidemic. This research seeks to identify regions where insufficient infrastructure impedes public healthcare systems from catering to dengue patients. The primary focus of this paper is to evaluate the necessary degree of public-private collaborations needed to address seasonal dengue epidemics and pinpoint specific durations within the healthcare system that require attention.

Blood is the indispensable circulating fluid for sustaining human life. On demand supply of quality blood is a big challenge for every government in all developing countries. Specially, in festive seasons and winter, supplying quality blood on time is a big medical challenge. On the other hand, the consequences of mismanaged blood donation camp may lead to excess supply of human blood units. Also, in some cases, it is being noticed that human blood units are getting corrupted in transit from the blood donation camp to the blood bank. Hence, several units of human blood are getting spoiled over the time due to mismanagement and/or maintenance. In this research, we have applied a lexicographic optimization based model for finding best available blood bank from the point of blood donation camp. Alternative taxicab geometry based paths are used for finding best possible shortest path from the blood donation camp to the blood bank.

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters.

Music is an essential "element" in our lives. With the rapid development of technology, the use of compact disks and tapes for listening various forms of music of different cultures has become obsolete. Because of the generation of considerable music data, the accurate classification of music data has become a critical topic of research, and the development of deep learning technology provides a solution for music classification. Transfer learning techniques are widely used in image recognition but are rarely used for music classification. In this study, a small music dataset is used for music classification using transfer learning.

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection.

Information and communication technologies have spread rapidly, and as a result, people's attention to computer skills has reached a level as never before. However, most conventional skill assessment tools are often multiple-choice types and ask only for PC knowledge but not PC skills. Meanwhile, skill assessment using eye tracking has already been realized in the medical field and has been proven to be a reliable tool for skill assessment and may apply to other fields. Therefore, unlike the knowledge-questioning type PC skill assessment, this study is to propose a method to measure an operator's real skill level using PC operation log data. In this study, we use the operator's PC operation logs to generate heatmaps and operation features and compare these data with standard data generated by the KML model in order to assess the operator's pc skills.

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.

This paper introduces a ridge detection algorithm from terrain maps represented by homogenous triangular dissections. This paper also introduces a data format of the triangular dissections and a concept of processing system.

Information technology has been advancing in many countries. In their daily lives, people encounter computerized systems in many situations and often take their operation for granted. If a system failure occurs temporarily, it is disadvantageous for the system operator and the user. The use of distributed and parallel processing systems has increased the prevalence of software failures due to resource sharing among processes. The purpose of this study is to verify the presence or absence of deadlock by using the validator called FDR for the processes of the LEGO® MINDSTORMS® EV3 robot while it climbs stairs.

Most NoSQL databases have been devised independently from each other with specific application requirements. This has resulted in developing separate own data model and query language for each NoSQL database. The lack of standards in data models and query languages make applications and data less portable using these databases. Further, absence of formal semantics in query languages inhibits a precise understanding of query over NoSQL databases. To handle these issues, in this paper, an ontology driven query language for NoSQL databases is proposed. The proposed query language provides an efficient and common abstraction over the operational aspects on various kinds of NoSQL databases. The language includes formal common syntax and semantics of distinct query operators of NoSQL databases and those are represented in Description Logic. Further, usefulness of proposed query operators are proved using a suitable case study.

Domestic and industrial pollutions affected the water quality to a greater extent. Polluted water became a major reason behind several community diseases, mainly in undeveloped and developing countries. The public health condition is deteriorating and putting an extra burden of countermeasures to prevent such water borne diseases from spreading. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality. However, the accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, multi-objective genetic algorithm was employed to train the artificial neural network (NN-MOGA) to improve its performance over its traditional counterparts. The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other, well established models namely NN-GA (ANN trained with Genetic Algorithm), NN-PSO (ANN trained with Particle Swarm Optimization) and SVM in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-MOGA over the other models.

Landscape modeling is important in computer graphics applications. In the field of computer graphics, representations for rectangular dissections are used in synthetic design processes. There are unavoidable problems with real-time rendering of non-uniform landscapes. One of them is ability of expression on terrain rendering. In this paper, we propose a data structure of rectangular dissections with heterogeneous cells and present a triangulation algorithm for the data structure with elevation data. The data structure possesses the potential for high-quality expression. The triangulation algorithm generates base triangular meshes for the data structure with consideration of visualization problems and produces a fundamental polygon model of the data structure for terrain maps.

Databases are mainly used in many systems in order to store data. Databases in large scale systems are difficult to grasp their logical structure, therefore, visualizing databases are quite important. To the present entity relationship diagrams (ER diagrams) have been proposed and used in designing and managing databases. However there are not so many tools which are based on formal mechanisms. In this paper, we propose a graph grammar for entity relationship diagrams which are based on formal mechanisms by using graph grammars. We also describe an application of the grammar.

Education curricula for children with developmental disabilities have attempted to include information and communication technology (ICT) teaching materials. However, such children demonstrate individual differences at the developmental stage of their cognitive faculties. Thus, it is difficult to adopt commercially available ICT teaching materials when working with them. In this study, we introduce the concept of inclusive design in the implementation of ICT teaching materials for children with developmental disabilities. Inclusive design allows for the participation of the elderly as well as those with disabilities at the early stages of development and can be used to identify special needs associated with the development. In addition to ICT teaching materials, the use of Extreme Programming Method for Innovative Software Based on Systems Design (EPISODE), which has agile development and innovation techniques at its core, has been proposed. Therefore, in this study, we attempt to develop a method comprising inclusive design and EPISODE to develop ICT teaching materials. Finally, we report on the practice of developing ICT teaching materials for children with developmental disabilities.

In the field of software engineering, a very old and important issue is how to understand the software. Understanding software means more than understanding the source code; it also refers to the other facts related to that particular software. Sometimes even experienced developers can be overwhelmed by a project's extensive development capabilities. In the development process, project leaders (PLs) have overall knowledge about the project and are keenly aware of its vision. Other members have only partial knowledge of the functions assigned to them. In this research, we propose a model to design ontology to support software comprehension and handle issues of knowledge management throughout the development process. By applying our methodology, understanding software and managing knowledge can become possible in a systematic way for open source and commercial projects. Furthermore, it will help beginners become more involved in a project and contribute to it in a productive way.

Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, we propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. We also explain our method's capabilities by comparing it to the sim plagiarism detector. Furthermore, we reveal how our method successfully detects the presence of plagiarism.

We investigate sets of conditions with respect to narrower drawing of tree-structured diagrams on an integral lattice. We found that under certain sets of conditions there are practical procedural algorithms for narrower drawing of tree-structured diagrams, while under other sets of conditions there are none. Based on our findings, we present efficient algorithms that provide narrower placement satisfying given amorphous conditions. In intractable conditions, we propose a constraint-based algorithm for drawing tree-structured diagrams with a minimum-width by limiting the number of cells. Our results provide a criterion for deciding under given conditions, whether to use procedural or constraint-based algorithms to draw a tree-structured diagram.