The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. Authors can electronically submit articles throughout the year using the Article Submission System. Authors can format their article either in (i) a single-column format or (ii) as per the journal template. The submitted articles should not have been previously published or are currently under consideration for publication elsewhere. The journal does not accept brief or short notes for publication. The editors retain the right to reject any articles that lack quality or originality without sending them for review. All articles must fall within the journal’s scope and will undergo a double-anonymized peer-review process. Authors must confirm that they have read and understood the content of their submitted article and ensure that it meets acceptable English grammar and usage standards. To help with the proofreading process, authors can use tools like Grammarly or similar applications. As an open-access journal, authors must pay an Article Processing Charge (APC) to publish their articles and retain copyright. Additionally, authors should familiarise themselves with the editorial and publishing policies of the journal.
CC-BY-NC-ND 4.0
Mars Explorer Experiments: Ideas from Data Mining Point of View
In the context of the "Mars Explorer Experiments," a set of autonomous agents (vehicles) must navigate an unknown, obstacle-filled terrain to locate and collect rock samples, with limited communication between agents and no prior detailed map of the planet. This paper explores the application of data mining techniques in the development of autonomous vehicle control architectures for Mars exploration, specifically focused on the task of collecting precious rock samples according to three types of agents (Cooperative Agents, Non-Cooperative Agents, Subsumption Architecture ) through describing the agent with the data mining point of view (problem statement in agent, solution using data mining algorithms, discussion problem and solution from data mining point of view). A significant portion of the paper discusses how data mining approaches such as clustering, reinforcement learning, anomaly detection, and pattern mining can be employed to improve agent coordination, exploration strategies, and real-time decision-making in dynamic and uncertain environments. Incorporating data mining algorithms into 'Mars exploration experiments' shows a hopeful way to boost the performance and decision-making abilities of autonomous agents on Mars. The paper shows that the data mining algorithms are not just beneficial but essential in developing intelligent, cooperative, and autonomous systems for Mars exploration vehicles.
Copyright (c) 2025 Dr. Mohammed Ali Mohammed, Dr. Nadhim Azeez Sayel, Noor Muneam Abbas (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Phishing websites have emerged as a serious security risk. Phishing is the starting point for many cyberattacks that compromise the confidentiality, integrity, and availability of customer and business data. Decades of effort have gone into developing novel methods for automatically identifying phishing websites. Modern systems aren't very adept at spotting new phishing threats and require a lot of manual feature engineering, even though they can produce better outcomes. Thus, an open problem in this discipline is to identify tactics that can swiftly handle zero-day phishing attempts and automatically recognize phishing websites. The web page that the URL hosts has a plethora of information that can be utilized to assess the maliciousness of the web server. One useful technique for spotting phishing emails is machine learning. Additionally, it does away with the drawbacks of the earlier approach. After a careful analysis of the literature, we proposed a novel approach that combines a machine learning algorithm with feature extraction to identify phishing websites. Using the gathered dataset, this study aims to train deep neural networks and machine learning models to detect phishing websites.
Copyright (c) 2024 Joshma K J, Vineetha Sankar P (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.
Copyright (c) 2024 Aaron Josey, 8457Amrutha N (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
For businesses and students alike, campus recruitment is an important occasion. While businesses aim to draw in the best employees, students eagerly anticipate beginning their professional careers. Salary prediction is a crucial component of college recruitment, when employers ascertain the wage ranges, they would offer prospective employees. Many criteria, including the candidate's qualifications, experience, and education, as well as the company's budget and industry norms, play a role in predicting the salary for campus recruitment. In this project, we'll apply machine learning approaches to forecast college recruitment salaries based on candidate historical data and salaries that match to those positions. In this project, we develop a predictive model for college recruitment by analysing the dataset that has been provided. Data processing and exploratory data analysis (EDA) are our initial steps. After that, we build a Flask web application that uses the trained predictive model to be deployed and lets users anticipate things based on input.
Copyright (c) 2024 Anupama P R, Nithin Sebastian (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Employee attrition occurs when a worker leaves a company to join another firm for a better offer. It might also be referred to as Employee Defection. Representative downsizing is likely to be significant when there is a pressing demand for workers in a particular industry due to mass retirements or organizational growth. At one point, the programming industry had significant attrition rates due to abundant job opportunities in the software sector driven by the demand for software products across all industries. Reducing the employee attrition rate is a challenging challenge faced by HR managers. This study provides a clear viewpoint on predicting employee turnover using Machine Learning methods. The projection is completed using data obtained from IBM HR analysis. We employed Logistic Regression for the analysis and achieved an accuracy rate of 87%.
Copyright (c) 2024 Benson Antony D V, Haritha Rajeev (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The proliferation of counterfeit currency poses a significant threat to both individuals and the national economy. While existing fake currency detection tools are primarily accessible to banks and large enterprises, everyday people and small businesses remain susceptible. Thus, this project aims to delve into the security features of Indian currency and develop a software solution leveraging advanced image processing and computer vision techniques to detect and neutralize counterfeit notes. Counterfeiting currency poses a genuine menace to both the populace's well-being and the nation's economic stability. Although counterfeit currency detection tools exist, their accessibility is typically confined to banking institutions and corporate entities, leaving ordinary citizens and small enterprises susceptible to fraud. Thus, this project endeavours to examine the diverse security attributes of Indian currency and subsequently craft a software-driven apparatus capable of discerning and nullifying counterfeit Indian currency through sophisticated image processing and computer vision methodologies. Notably, this currency authentication system will be meticulously crafted using the Python programming language within the Jupyter Notebook framework.
Copyright (c) 2024 Aneena Babu, Vineetha Sankar P (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Text to Image Conversion using Stable Diffusion
In this paper, we introduce a pioneering technique for translating textual descriptions into visually compelling images using stable diffusion methods, with a particular emphasis on the latent diffusion model (LDM). Our approach represents a departure from conventional methods like Generative Adversarial Networks (GANs) and Attn GAN, offering enhanced accuracy and diversity in the generated images. Through extensive experimentation and comparative analysis, we validate the efficacy of our method. Leveraging the LAION-5B dataset, we fine-tune the stable diffusion model, resulting in superior performance in text-to-image conversion tasks. Our findings underscore substantial advancements in accuracy, showcasing the promise of stable diffusion-based approaches across a spectrum of applications. By embracing stable diffusion techniques, we overcome some of the limitations encountered in previous methodologies. This enables us to achieve a higher fidelity in image generation while maintaining a diverse output spectrum. Our method excels in capturing intricate details and nuances specified in textual descriptions, facilitating a more faithful translation from text to image. The significance of our work extends beyond mere technical improvements. By pushing the boundaries of image synthesis, we contribute to the evolution of artificial intelligence, fostering new possibilities for creative expression and content generation. Our approach not only enhances the capabilities of AI systems but also democratizes the process of image creation, empowering users to effortlessly translate their ideas into visually stunning representations. Through our research, we aim to inspire further exploration and innovation in the realm of text-to-image conversion. The success of stable diffusion-based methods underscores their potential to revolutionize various domains, including computer vision, graphic design, and multimedia content creation. As we continue to refine and optimize these techniques, we anticipate even greater strides in the field of AI, ushering in a new era of intelligent image synthesis and interpretation.
Copyright (c) 2024 Ashy Correya, Amrutha N (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework’s effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.
Copyright (c) 2024 Rajalakshmi B, Nithin Sebastian (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Sign Language to Text Conversion using CNN
Sign language is a communication strategy used by those who are unable to hear. So those people who know sign language can communicate with people who are deaf. But a majority of our people don’t know sign language therefore there comes a communication gap between the ones who know sign language and others who don’t know. This project’s major purpose is to bridge this gap by developing a system that recognizes multiple sign languages and translates them into text in real-time. We use machine learning technologies to construct this system especially, convolutional neural networks (cnns), which are used to recognize and translate American Sign Language (ASL) into text by capturing it using a webcam. The transformed text is then presented on the screen by which individuals can comprehend and communicate with those who use sign language. The system’s performance is evaluated on a dataset of ASL gestures, attaining excellent accuracy and indicating its potential for practical usage in enhancing communication accessibility for the deaf and hard-of-hearing community.
Copyright (c) 2024 Alan Wilson, Lenet Steephen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The amount of textual data has grown dramatically over time, opening up new avenues for machine learning (ML) and natural language processing (NLP) study. These days, sentiment analysis of comments on YouTube is a really fascinating subject. Although there are a lot of user reviews and comments on many of these films, the low consistency and quality of the material in these comments has prevented much work from being done in terms of identifying trends from them thus far. In this research, we use machine learning techniques and algorithms to perform sentiment analysis on YouTube comments pertaining to popular themes. We show that a clear picture of how real-world events affect public sentiment can be obtained by analyzing the attitudes to identify trends, seasonality, and projections. The findings indicate a strong correlation between the sentiment trends of users and the actual occurrences linked to the corresponding keywords. This study uses a YouTube extractor to perform sentiment analysis on comments on YouTube using citation sentences.To remove the noise from the corpus of comments, various data normalization algorithms were applied to the data. We created a system using six distinct machine learning techniques, including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), to perform classifying on this data set.
Copyright (c) 2024 Aiswarya A S, Haritha Rajeev (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Lipreading is the task of decoding text from the movement of a speaker’s mouth. This research presents the development of an advanced end-to-end lipreading system. Leveraging deep learning architectures and multimodal fusion techniques, the proposed system interprets spoken language solely from visual cues, such as lip movements. Through meticulous data collection, annotation, preprocessing, model development, and evaluation, diverse datasets encompassing various speakers, accents, languages, and environmental conditions are curated to ensure robustness and generalization. Conventional methods divided the task into two phases: prediction and designing or learning visual characteristics. Most deep lipreading methods are trainable from end to end. In the past, lipreading has been tackled using tedious and sometimes unsatisfactory techniques that break down speech into smaller units like phonemes or visemes. But these methods often fail when faced with real-world problems, such contextual factors, accents, and differences in speech patterns. Nevertheless, current research on end-to-end trained models only carries out word classification; sentence-level sequence prediction is not included. LipNet is an end-to-end trained model that uses spatiotemporal convolutions, a recurrent network, and the connectionist temporal classification loss to translate a variable-length sequence of video frames to text. LipNet breaks from this traditional paradigm by using an all-encompassing, end-to-end approach supported by deep learning algorithms, Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are skilled at processing sequential data and extracting high-level representations, are fundamental to LipNet’s architecture.LipNet achieves 95.2% accuracy in sentence-level on the GRID corpus, overlapped speaker split task, outperforming experienced human lipreaders and the previous 86.4% word-level state-of-the-art accuracy.The results underscore the transformative potential of the lipreading system in real-world applications, particularly in domains such as assistive technology and human-computer interaction, where it can significantly improve communication accessibility and inclusivity for individuals with hearing impairments.
Copyright (c) 2024 Jishnu T S, Anju Antony (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Pre-Processing and Normalization of the Historical Weather Data Collected from Secondary Data Source for Rainfall Prediction
In the twenty first century, data analysis has become the talk of the town. Almost every company or organization depends on data analysis for taking future decision. The most important step in data analysis after data collection is the preprocessing of the collected data. The main aim of data analysis is to find meaningful pattern by processing large amount of data. In data preprocessing, the inconsistency of collected data has been removed. After storing data for a relatively longer period, it becomes noisy and inconsistent. While measuring various parameter due to error in the instrument or human error, the value become incorrect or invalid. It is necessary to remove the invalid data otherwise it will deflect the results and produce error in the prediction. In this work preprocessing of the weather data has been analyzed for rainfall prediction using data mining.
Copyright (c) 2024 Deepak Sharma, Dr. Priti Sharma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Comparison of the Proposed Rainfall Prediction Model Designed using Data Mining Techniques with the Existing Rainfall Prediction Methods
Weather prediction is a very old practice and people are doing predictions about weather much before the discovery of the weather measuring instrument. In ancient times, people give weather predictions by observing the sky for a long time and patterns of the stars at night. Things are a bit different now. People more relay on the past trends and patterns followed by the weather parameters. Data mining and machine leaning is used to analysis the historical weather trends by analyzing weather data using various Data mining techniques. In this paper three rainfall prediction model based on data mining techniques are proposed and compared with the other rainfall prediction model. The comparison has been done on the basis of accuracy, precision, Recall and RMSE. The proposed models are based on ensemble methods such as bagging, boosting, and stacking. Ensemble methods are used to enhance the overall performance and accuracy of the prediction. In both bagging and boosting based proposed rainfall prediction models, artificial neural network is used as a base leaner and daily weather data from the year 1988 to 2022 is used. In stacking based proposed rainfall prediction model, random forest, Logistic regression, and K-Nearest neighbor are used as base leaners or level -0 learners and Artificial neural network is used as Meta model.
Collection of Weather Data from Authentic Websites and Secondary Data Sources for Rainfall Prediction
The field of data mining and machine learning has been grown many folds from the last two decades. Almost every other problem can be solved using data mining and this becomes the most tempting part of it for the scientist and researchers all over the world. Data mining can be viewed as a process of discovering knowledge. This discovery of knowledge starts with the collection of data and ends with the acquired knowledge in the form of patterns. Data collection lays the foundation for the process of knowledge discovery. In this paper, various secondary data sources from where data can be collected for rainfall prediction are deeply studied and analyzed. Some of these authentic websites and secondary data sources are NCDC (National climate data center), Kaggle, Datahub.io, UCI machine learning repository, Earth Data etc. The data collected from these secondary data sources for rainfall prediction have been critically analyzed and compared on the parameters of Accuracy, Completeness, reliability, relevance, and timeliness.
The Myers-Briggs Type Indicator (MBTI) is one of the most commonly used tool for assessing an individual’s personality. This tool allows us to identify the psychological proclivity in the way they take decisions and perceive the world. MBTI has it’s applications spread across several fields which include career development and personal growth. This test consists of a set of questions which are specifically designed to evaluate and measure an individual’s choices based on four dichotomies – Extraversion (E) vs. Introversion (I), Sensing (S) vs. Intuition (N), Thinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P). Myers-Briggs Personality Prediction project aims to develop and deploy a system using machine learning which is capable of predicting one’s MBTI personality type based on their online written interactions such as social media posts, comments, blogs etc. This project has significant implications for various applications, including improving customer experience, optimizing team dynamics, and developing personalized coaching programs. Through this project, we hope to gain a deeper understanding of how language use and personality type are related and to develop a robust tool for personality prediction.
Copyright (c) 2024 Rohith Muralidharan, Neenu Kuriakose, Sangeetha J (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Human-Computer Interaction (HCI) is no longer the sole study of information technology or computer science but now it has covered the area of medical, entertainment, etc. As the application of HCI is increasing, so is the requirement of students to work in a multidisciplinary environment. Making students comfortable working in a multidisciplinary environment is not an easy task. The students are required to make pretty much aware and comfortable with the underlying problem statement. The evolution of HCI in education is to make sure that students can understand the concepts and working of the model in a more effective way. The goal of this project is to create a web-based e-Learning tool, ‘Path Finding Visualizer’. It refers to computing an optimal route between the specified start node and goal nodes visualizing shortest path algorithms. The conceptual application of the project is illustrated by the implementation of algorithms like Dijkstra’s, A*, and DFS. The end product is a web application so that any user can easily see and learn the working of the algorithms through perceivable visualizations. The user-friendliness of the project provides the user with easy instructions on how to operate it. The initial results of using the application show promised benefits of the e-Learning tool towards students getting a good understanding of shortest paths algorithms.
Copyright (c) 2024 Ananya Khurana, Rohan Raj, Satender Kumar, Ms. Neha Garg (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Overview on Data Mining and Data Fusion
Strong adoption of Internet and Communication technologies across industries in the last two decades has led to large-scale digitization of business processes. While this has helped in the instant availability of information, over the period, the source and amount of this information have increased multi-fold giving rise to Big Data. With the increase in volume, the relevance of data in its raw format continues to decrease over time. According to HACE Theorem, Big Data has autonomous sources being distributed and decentralized data in a complex relationship with each other. Making sense of this ever-growing large pool of data has become increasingly difficult and has created a new problem waning the initial gains made via the digitization of systems and processes. This gave rise to the evolution of multiple Data Mining techniques that have helped to classify large volumes of data into relevant segments and drive value to help provide meaningful information. To extract and discover knowledge from data, Knowledge Discovering Databases (KDD) help in the refining of data. This paper discusses various data mining techniques that help to identify patterns and relationships to help make business decisions using data analysis. Furthermore, the Data Fusion method is reviewed which deals with joint analysis of multiple inter-related datasets providing multiple complementary views to help further with precise decision-making.
Copyright (c) 2024 Vinayak Jain (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Usage of Technology in Promoting Well-being of Senior Citizens
In the present existence, with advancements taking place and with the advent of modernization, the utilization of technologies have acquired prominence. The internet is regarded as one of the eminent factors that is utilized in augmenting information in terms of all subjects and concepts. Furthermore, individuals are able to obtain answers to all types of questions that are overwhelming to them. The senior citizens make use of technologies and internet for number of purposes. In addition, they are required to obtain help from others as well in putting into operation different tasks and activities in a well-ordered manner. In cases of visual impairments and other types of health problems and illnesses, the senior citizens are unable to carry out job duties on their own, hence, they are required to obtain help from others. The senior citizens are not only incurring the feeling of satisfaction, but they are able to contribute efficiently in leading to up-gradation of overall standards of living, when they are making use of different types of technologies and internet. The senior citizens in some cases are overwhelmed by feelings by apprehensiveness and vulnerability. But understanding the concepts and getting engaged in regular practice will be facilitating in honing technical skills. Therefore, the role of technology is considered important in promoting well-being of senior citizens. The main concepts that are taken into account in this research paper are, understanding the meaning and significance of technologies, factors highlighting usage of technology in promoting well-being of senior citizens and measures to be implemented in augmenting technical skills by senior citizens.
Copyright (c) 2024 Dr. Radhika Kapur (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is “Bank customer churn prediction” is to build a model that can distinguish and visualize which factors or attributes contribute to customer churn. In addition to that, this paper also discusses a comparison between various classification algorithms. Machine learning is a modern technology that has the potential to solve classification problems. Using supervised machine learning techniques, a best model is chosen that will assign a probability to the churn to simplify customer service to prevent customer churn. Few methodologies are compared in order to accomplish different accuracy levels. XGBoost is considered in order to check if a better model can be obtained that provides best result in terms of accuracy. The other three machine learning algorithms compared are Logistic regression, Support vector machine [SVM], and Random Forest.
Copyright (c) 2024 Dr. Ayan Chattopadhyay, Mr. Mukul Basu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Unsupervised Learning Based Brand Sentiment Mining using Lexicon Approaches A Study on Amazon Alexa
Consumer sentiment analysis has gained immense attention in the recent past. The abundance of data in today’s world, especially those generated from the social media platforms, has triggered sentiment exploration like never before. The analysis of consumer sentiments have indeed helped organizations in effective decision making worldwide. In the communication technology domain, voice activated virtual assistants (VAVAs) are one of the latest entrants and they are gaining immense popularity by the time. Brand sentiment studies on VAVAs being limited in number creates an opportunity to explore further. This study fits into the domain of sentiment mining and the purpose of the paper is to review the consumer sentiment towards the global leader brand in the voice activated virtual assistant product segment, Amazon Alexa. Of the various approaches available, the researchers chose unsupervised learning based lexicon approach to estimate the brand sentiment. Three popular lexicon based sentiment classifiers, TextBlob, VADER and AFINN, have been used in the present context for exploration purpose. To the best of the knowledge of the researchers, this research effort includes, for the first time, multiple lexicon based approaches in exploring the sentiment towards the brand Alexa. This study shows consumers to have a significantly positive sentiment towards the chosen brand. The output from the three comparative classifiers reveal similar results which also validates the robustness of the outcomes and that of the chosen methods. The study anticipates a bright sales potential of the brand. Also, the use of alternative lexicon approaches is expected to enrich the existing literature in the sentiment mining domain.
Criticality Trend Analysis Based on Different Types of Accidents using Data Mining Approach
Safety on roads and prevention of accidents are the prime concern of any highway system. Data mining is a source of retrieval of information for knowledge discovery approach. Many data mining methodologies have been applied to accident data in the recent past years. There is need to analyze the relationship between different factors related to accidents i.e. number of persons affected by fatal, minor, grievous, non-injury, road feature (ROF), road condition (ROC), cause of accident (CAU) and vehicle responsible (VR) according to daily, fortnightly, semi-fortnightly and monthly basis. The objective of this study is divided into three sub-objectives. The First sub-objective of this study is to divide number of accident dataset of National Highway sections of Karnataka state implemented by Project Implementation Unit i.e. PIU (Bangalore, Chitradurga, Dharwad, Gulbarga, Hospet and Mangalore) during January 2012 to January 2017 collected from NHAI (National Highway Authority of India) in homogeneous clusters using K-means clustering. The second sub-objective is to reflect the relationship between different factors i.e. a number of persons affected by fatal, minor, grievous, non-injury, CAU, ROC, ROF and VR using Apriori association rule. The last sub-objective is to perform temporal trend analysis for each cluster on the basis of rules generated by Association Rule Mining.
Association Rule Mining and Temporal Trend Analysis
Copyright (c) 2024 Kumari Pritee, R.D. Garg (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Implementation of Rainfall Prediction Model using Supervised Machine Learning Data Mining Techniques
Data mining is a rapidly developing technology that has enriched a lot of field such as business analysis, market analysis, weather forecasting, stock market analysis and many more. It starts with collecting data sets from reliable sources and pre-processing that data. There are some anomalies associated with data collected in large volumes such as outliers, missing values, and duplicated values. Remove these kinds of anomalies is teamed as pre-processing of data. In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ANN classification techniques is a complex data mining technique results in high accuracy in prediction of rainfall.
Impact of Swear and Negative texts on Social Media Users
Nowadays, the use of social media has increased exponentially. People show different behavior on social media depending on the kind of responses and behavior of people around them. It is important now to analyze the behavior of social media users and the way how they affect their friends. In this paper, behavioral analysis of people is done based on Twitter data. An algorithm is proposed which helps in finding the impact of text written by someone on social media and its effect on others. The impact of written text is calculated with the help of the number of retweets done for the same tweet. The severity of the used word is calculated based on AFINN dictionary. According to the proposed algorithm, the score of the dictionary is recalculated when a negative word is forwarded multiple times. This is done with the understanding that if a less severe negative word is used many times, it may affect the person in a highly negative manner. With this, Severity of words is recalculated and its impact on people is found with the help of the proposed algorithm. The impact of using negative words on social media affect 32 % of the total users (in their friend-list). Behavior change is demonstrated with the help of graphs week-wise, month-wise and year-wise analyses. The research helps in finding the impact of swear words on social media users depending on the frequency and severity score of the words.
Copyright (c) 2024 Srishty Jindal, Dr. S.V.A.V. Prasad, Dr. Kamlesh Sharma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Mineral and Mining industry is a huge energy requiring sector which demands connections to a viable electric power origin and reference. With the upsurge of these mineral requests and decreasing valued grades of ores, energy aspiration is approximated to sky-rocket to 36% by the year 2035. It is even projected to accelerate geometrically from the fact that sophistication and powering of the mine locations speeds up the stability into the necessity of energy applications and its requirements derived from fossil fuels utilized to generate electricity. This paper discusses other fossilized fuel-based materials for generation of electricity.
Copyright (c) 2024 Amosu C. O. (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy
Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures.
Copyright (c) 2024 Akshansh Mishra, Tarushi Pathak (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Copyright (c) 2023 Deepak Sharma, Dr. Priti Sharma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0