Implementation of Movie Recommendation System Using Hybrid Filtering Methods and Sentiment Analysis of Movie Reviews
content based filtering; cosine similarity; count vectorizer; demographic filtering; movie metadata; movie reviews; natural language processing; recommendation system; sentiment analysis
In present era of digitization of entertainment, immense volume of movies are produced, which results in the necessity of sophisticated recommendation systems. In the streaming platform these systems empower users to discover new and relevant movies, benefiting both viewers and the entertainment industry. This research paper offers a comprehensive method for incorporating movie review sentiment analysis into a hybrid recommendation system. The study focuses on 4890 movies using a broad dataset containing the detailed descriptions of the movies along with the reviews. To employ the demographic filtering, the popularity score of the movies were calculated, then to apply the collaborative filtering, the textual movie descriptions were vectorized using the countvectorizer method. To predict the sentiment of the movie reviews, the high accuracy model "ControX/Sen1"was used. This hybrid recommendation system ranked the movies based on the user's preferences by employing cosine similarity, the sorted list was further filtered with the positive sentiment reviews. By including sentiment analysis, this research advances sophisticated movie recommendation systems by providing a comprehensive method for addressing user preferences and emotional resonance in film selections. 2024 IEEE.
Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 513-518.
Institute of Electrical and Electronics Engineers Inc.
Naskar I., Christ (Deemed to Be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India; Joseph N.P., Christ (Deemed to Be University), Department of Statistics and Data Science, Karnataka, Bengaluru, India
Identification of Student Programming Patterns through Clickstream Data
clustering; computer programming; educational data mining; regression; student programming patterns
In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. This study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. Through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. 2024 IEEE.
Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 1153-1158.
Gupta A., Bennett University, Scset, Greater Noida, India; Jindal M., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Goyal A., Bennett University, Scset, Greater Noida, India
Gems of Prediction: From Clarity to Carats - Unveiling Diamond Prices with Machine Learning in Waikato Environment for Knowledge Analysis
Bagging; Data Mining; M5P; REP Tree; Weka; Weka Environment
Background: This research focuses on using Weka's toolkit to test machine learning models for predicting diamond prices. The complexity of diamond value characteristics, such as carat, cut, color, and clarity, motivates the study to find the most accurate models. The goal is to promote fairer market processes and customer education. Methods used: The research rigorously preprocesses a diamond attributes dataset using Weka for analysis. Various machine learning algorithms are examined, including simple algorithms like Decision Stump and ZeroR, sophisticated models like M5P and REP Tree, and advanced ensemble approaches like Bagging with REP Tree. Model performance is evaluated using train/test splits (80-70-60%) and cross-validation (5-fold and 10-fold) with metrics such as Correlation Coefficient, MAE, and RMSE. Results achieved: The research finds that ensemble approaches, particularly Bagging with REP Tree, outperform simple and sophisticated models in diamond price prediction. These techniques demonstrate higher accuracy and lower error rates, highlighting the need for multiple models to capture the complexity of diamond valuation. Simple models provide benchmarks and insights into dataset trends but are less precise. Concluding remarks: This study contributes to the understanding of machine learning algorithms for diamond price prediction, an important economic valuation subject. It demonstrates the effectiveness of complex data analysis methods using Weka. The research also highlights the accessibility and sophistication of machine learning at the crossroads, with Weka's cutting-edge algorithms making complicated analytical methods more accessible for practical, everyday use. This work adds to the knowledge of the dynamics of diamond prices and the role of machine learning in economic research. 2024 IEEE.
Basha M.S.A.; Oveis P.M.; Raju R.P.K.; Sucharitha M.M.
International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024
https://doi.org/10.1109/ICDCOT61034.2024.10515908 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193246649&doi=10.1109%2fICDCOT61034.2024.10515908&partnerID=40&md5=ec6f6ef3bebd2cdb6fd0ef8a2402c424
Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Oveis P.M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India
Blockchain Computing: Unveiling the Benefits, Overcoming Difficulties, and Exploring Applications in Decentralized Ledger Infrastructure
Blockchain-based applications; Distributed ledger; Intelligent medical care; Peer-to-peer system
The protocol known as blockchain, which is composed of blocks, utilizes a decentralized distributed system of nodes (miners). There are three parts to every block: information, which is represented by a hash, and the hash of a previous transaction. In order to regulate data after it has been stored, it is quite difficult to make changes. Mining is compensated for each encrypted function computation they carry out to verify the transaction. This research paper will provide a comprehensive understanding of blockchain-based technologies and how they are applied in a variety of industries, including those that deal with digital currencies, financial services, medical manufacturing, privacy, and a number of other fields. Digital money, notably the cryptocurrency Bitcoin, had previously been one of the most well-known network applications. As there have lately been several studies about the unique utilization of this sort of technology, we will discuss some of these academic works as well as the challenges encountered during the development of these kinds of applications. Blockchain technology is a quickly growing area of database technology that has recently found use in a wide range of industries, including the use of digital money, hospital administration, and other academic subjects. Because of how blockchain technology works and operates, these types of applications are now possible. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Poonia R.C.; Verma A.; Divakaran P.; Mittal S.; Vats P.; Biswas S.S.
Lecture Notes in Networks and Systems, Vol-890, pp. 397-410.
Springer Science and Business Media Deutschland GmbH
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma A., Department of CSE, ADGITM, New Delhi, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Biswas S.S., Department of CSE, SCSE, Jamia Hamdard University, New Delhi, India
An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach
Assets; Deep reinforcement learning; Optimal Return; Optimization; Portfolio selection; Stock price prediction
Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE.
Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 117-123.
https://doi.org/10.1109/ICWITE59797.2024.10503521 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562600&doi=10.1109%2fICWITE59797.2024.10503521&partnerID=40&md5=4955509a44cd3910fb93bea3a7ac2ab9
Ramaiah K., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India; Soundarabai P.B., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India
Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence
BERT; Deep Learning; Machine Learning; Natural Language Processing; Text Classification
In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE.
Proceedings - 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024, pp. 466-473.
https://doi.org/10.1109/ICICV62344.2024.00079 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193255348&doi=10.1109%2fICICV62344.2024.00079&partnerID=40&md5=b7e7cd69387a5bf8266796b9fd4c193d
Nagendra N., Christ (Deemed to Be) University, Department of Computer Science., Bengaluru, India; Chandra J., Christ (Deemed to Be) University, Department of Computer Science., Bengaluru, India
Investigation on Preserving Privacy of Electronic Medical Record using Split Learning
Healthcare; Patient data; Privacy; Security; Split Learning
Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. 2024 The Authors. Published by Elsevier B.V.
Kiruthika M.; Kumar A.; Krishnasamy L.; Sarveshwaran V.
Procedia Computer Science, Vol-233, pp. 614-622.
https://doi.org/10.1016/j.procs.2024.03.251 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192433820&doi=10.1016%2fj.procs.2024.03.251&partnerID=40&md5=96f40b4d76e04e80ad703a12a76b9e4d
Kiruthika M., Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, 603203, India; Kumar A., Punjab National Bank, New Delhi, 110075, India; Krishnasamy L., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, 560074, India; Sarveshwaran V., Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, 603203, India
A Novel Ensemble based Model for Intrusion Detection System
accuracy; Intrusion Detection System; Network security; Random Forest algorithm
In the present interconnected world, the increasing reliance on computer networks has made them susceptible to multiple security threats and intrusions. Intrusion Detection Systems (IDS) is essential for shielding these networks by detecting and mitigating potential threats in real-time. This research paper presents an in-depth study of employing the Random Forest algorithm for building an effective intrusion detection System. The proposed IDS uses the power of the Random Forest algorithm, a popular ensemble learning technique, to detect various types of intrusions in network traffic effectively. The algorithm integrates more than one decision trees to produce a robust and accurate classifier, capable of handling large-scale and complex datasets typical of network traffic. The proposed system can be used in various industries and sectors to protect critical assets, ensuring the uninterrupted operation of computer networks. Evolving cyber threats have encouraged further research into ensemble analytics methods to increase the resilience of Intrusion Detection Systems in an ever-changing threat landscape. 2024 IEEE.
Bhattacharya N.; Subudhi A.; Mishra S.; Sharma V.; Aderemi A.P.; Iwendi C.
Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 620-624.
Bhattacharya N., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Subudhi A., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Aderemi A.P., University of Bolton, School of Creative Technologies, United Kingdom; Iwendi C., Christ (Deemed to Be University), Delhi NCR, India
Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches
Machine Learning approaches; Solar Energy; Time Series and ARIMA
In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE.
Sucharitha M.M.; Sowjanya S.; Sumalatha K.N.; Ayesha S.; Basha M.S.A.
Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 235-241.
Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management Studies, Nandyal, India; Sumalatha K.N., Maris Stella College, Vijayawada, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India
Artificial Intelligence-Based L&E-Refiner forBlind Learners
Artificial intelligence; Higher education; Learners; Natural language processor; Scribe; Visually challenged
An Artificial Intelligence (AI)-based scribe known as L &E Refiner for blind learners is a technology that utilizes natural language processing and machine learning techniques to automatically transcribe lectures, books, and other written materials into audio format. This system is designed to provide an accessible learning experience for blind students, allowing them to easily access and interact with educational content. The AI scribe is able to recognize and understand various forms of text, including handwriting, printed text, and digital documents, and convert them into speech output that blind learners easily comprehend. This technology has the potential to significantly improve the accessibility and inclusion of education for blind individuals. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-879, pp. 439-448.
Vinay M., Christ University, Karnataka, Bangalore, India; Jayapriya J., Christ University, Karnataka, Bangalore, India
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE.
Ashish Joseph V.A.; Prathap B.R.; Kumar K.P.
IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024
https://doi.org/10.1109/ICDECS59733.2023.10502699 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192513626&doi=10.1109%2fICDECS59733.2023.10502699&partnerID=40&md5=2d1a2eb8b641699c7aaa793ff4eaf4a6
Ashish Joseph V.A., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Kumar K.P., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
anomaly detection; big data; cloud computing; deep autoencoder; encryption; security
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE.
Roy S.; Nijaguna G.S.; Shankar S.S.; Adnan M.M.; Umaeswari P.
Roy S., Christ (Deemed to Be University), Department of Electronics and Communication Engineering, Bangalore, India; Nijaguna G.S., S.E.A. College of Engineering and Technology, Department of Information Science and Engineering, Bangalore, India; Shankar S.S., Nitte Meenakshi Institute of Technology, Dept of Ece, Bengaluru, India; Adnan M.M., The Islamic University, Najaf, Iraq; Umaeswari P., R.M.K. Engineering College, Department of Computer Science and Business Systems, Tamil Nadu, Kavaraipettai, India
ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
Bag of Words model; CoSim Rank; Lin similarity; RNN
There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations. 2024 IEEE.
Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 594-599.
Deepak G., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Iyer K., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Shukla S., School of Engineering, Christ (Deemed to Be University), Department of Cse, Kengeri Campus, Bengaluru, India
Effective Techniques Non-linear Dynamic Model Calibration using CNN
Calibration; Convolutional Neural Network; Mapping; Nonlinear Dynamic
This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability 2024 IEEE.
Devanathan M.; Prasannan D.; Singh P.; Joshua O.; Adity Pai H.; Jakhar P.
Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 832-836.
https://doi.org/10.1109/IC2PCT60090.2024.10486219 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191178255&doi=10.1109%2fIC2PCT60090.2024.10486219&partnerID=40&md5=aa8bdcf2e0dca0c7077cd1e4b8ba76b9
Devanathan M., Christ University, Karnataka, Bengaluru, India; Prasannan D., Karpagam College of Engineering, Department of Civil Engineering, Coimbatore, India; Singh P., Sgt University, Faculty of Science, Department of Physics, Gurugram, India; Joshua O., Afe Babalola University Ado-Ekiti, Civil and Environmental Engineering, Ekiti, Nigeria; Adity Pai H., Graphic Era Deemed to Be University, Department of Computer Science & Engineering, Uttarakhand, Dehradun, India; Jakhar P., Indian Institute of Technology Jodhpur, Rajasthan, Jodhpur, India
Handwritten Telugu Character Recognition Using Machine Learning
character recognition; Convolutional Neural Network (CNN); Handwritten Telugu character recognition
The Telugu language is the most prominent representative within the Dravidian language family, predominantly spoken in the southeastern regions of India. Handwritten character recognition in Telugu has significant applications across diverse fields such as healthcare, administration, education, and paleography. Despite its importance, the Telugu script differs significantly from English, presenting distinct challenges in recognizing characters due to its complexity and diverse character shapes. This study explores the application of machine learning, particularly delving into deep learning techniques, to improve the accuracy of Telugu character recognition. This paper proposes a model to recognize handwritten Telugu characters using Convolutional Neural Network (CNN). The proposed study demonstrates the accuracy in identifying diverse handwritten Telugu characters. We assess the system's performance against conventional and machine learning methodologies and preprocess an extensive dataset to guarantee strong model training. The proposed model excels in accurately predicting visually similar but distinct characters, achieving an impressive accuracy rate of 96.96%. 2024 IEEE.
Karapu B.M.; Anoop G.L.; Elappila M.; Mithun B.N.
Karapu B.M., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Anoop G.L., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Elappila M., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Mithun B.N., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
Dynamic Time Warping (DTW); Long Short Term Memory (LSTM); Plant Disease Detection
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE.
Chaturvedi A.; Nadgaundi S.K.; Raja M.K.; Dubey V.R.; Singhal A.; Gupta V.
2024 3rd International Conference for Innovation in Technology, INOCON 2024
Chaturvedi A., Gla University, Department of Electronics and Communication Engineering, Uttar Pradesh, Mathura, India; Nadgaundi S.K., Bharati Vidyapeeth College of Engineering, Instrumentation Engineering Department, Navi Mumbai, India; Raja M.K., Sri Eshwar College of Engineering, Department of Computer Science and Engineering, Coimbatore, India; Dubey V.R., Jai Narain College of Technology, Department of Computer Science and Engineering, Lambakheda, Bhopal, India; Singhal A., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Gupta V., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India
Binary Logistic Regression; Demographic Characteristics; Environmental Education; Higher Education Institutes; Sustainable Society
The need to preserve the environment, lower pollution levels, expand the amount of green space, and encourage environmentally responsible behavior has grown in recent years, all of which will contribute to a more sustainable society. This study seeks to determine the probability that demographic variables of students in higher education in Delhi NCR will influence their desire to participate in environmental education. Binary Logistics Regression has been used on the data gathered from 302 respondents and the model has been found to have been a good one as shown by Omnibus Test. It is found that 'Gender' and 'Field of Study' are the two most significant variables, which have a higher probability impact on students' willingness to join environmental education. Specifically, female students vis-vis male students and students with engineering & and science background vis-vis other students have more chance of joining environmental education courses. 2024 IEEE.
2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024
https://doi.org/10.1109/ICRITO61523.2024.10522454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193787815&doi=10.1109%2fICRITO61523.2024.10522454&partnerID=40&md5=8bb16863e4cdda0e4eeb0c906132644e
Ganguly D.G., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India; Sinha D., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India; Das T., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Ability to monitor; Food surveillance; Monitoring system; Monitoring system; Security
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 555-563.
Kumar S., Department of LPM, Bihar Animal Sciences University, Patna, India; Kumari S., Department of LPM, Bihar Animal Sciences University, Patna, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Vats P., Department of CSE, Manipal University Jaipur, Rajasthan, Jaipur, India
Cultivating Digital Fields: A Cloud-Centric Blueprint for Stakeholder Engagement in the Indian Agriculture
Cloud computing; E-agriculture; E-governance; ICT in agriculture; Stakeholder engagement
This paper examines the potential of cloud computing to revolutionize the Indian agricultural sector, government operations, and rural connectivity. It highlights the benefits and challenges associated with cloud computing in agriculture and proposes a structured model to implement it effectively. Cloud computing allows farmers to access real-time information, make informed decisions, and improve access to markets. The paper examines the difficulties and advantages of cloud computing for the government in transitioning to a cloud-based version of itself for its operations. Additionally, it draws attention to specific areas related to the agricultural sector in India and certain applications offered by the government to enhance the consumer experience for stakeholders. The Government of India has demonstrated its commitment to developing technology-driven agriculture through e-NAM, Kisan Suvidha, and Agri-market initiatives. However, some challenges must be addressed to ensure the successful adoption of cloud computing in the agricultural sector. The proposed implementation model outlines the essential stages of the process, including the needs assessment, the selection of cloud providers, the automation of workflow, the modernization of applications, the implementation of security measures, and the implementation of continuous improvement. The model emphasizes the importance of training, feedback mechanisms, and collaboration. Furthermore, the paper underscores the need for a specific feedback system and grievance redress for agricultural cloud applications to enhance user experiences. To reap the full benefits of cloud computing in the Indian agricultural sector, a comprehensive strategy is necessary. This strategy necessitates technology adoption, awareness-raising, education, and stakeholder engagement. Utilizing cloud technologies, the Indian agricultural sector can realize sustainable growth, increased efficiency, and equitable development. This paper emphasizes the importance of cloud computing in transforming the Indian agrarian landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-916 LNNS, pp. 243-252.
Mitra S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India; Agrawal A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India; Joseph J.J., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
AI-based; Contact pressure; Fatigue life; FEM analysis; Hysteresis stressstrain; Spur gear; Structural analysis
The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Dwivedi R.; Sharma S.; Patil S.S.; Datere G.; Upreti K.; Haque M.
Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 535-544.
Dwivedi R., Department of Mechanical Engineering, Sagar Institute of Science and Technology, MP, Taipei City, Bhopal, India; Sharma S., Department Mechanical Department, AEG Consultancy Services Pvt. Ltd, Bhopal, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Datere G., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India