An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
DCNN; Deep Learning; Fibroids; Magnetic resonance imaging; Ultrasonography
The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. 2024 IEEE.
Kaushik R.; Sharma K.P.; Malik S.; Shanbhog M.
2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024
Institute of Electrical and Electronics Engineers Inc.
Kaushik R., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India; Sharma K.P., Gla University, Department of Computer Engineering and Application, Mathura, India; Malik S., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India; Shanbhog M., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Deep learning; Geographical features segmentation; Remote sensing; U-Net model; UNet++; UNetFormer
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 325-333.
Springer Science and Business Media Deutschland GmbH
Antony A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India; Kumar R.G., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India
Investigating Personalized Learning Paths to Address Educational Disparities Using Advanced Artificial Intelligence Systems
Adaptive Algorithms; Bias Mitigation; Content Embedding; Cosine Similarity; Cultural Nuances; Cultural Relevance Score; Cultural Sensitivity; Data Auditing; Diversity in Training Datasets; Named Entity Recognition (NER); Natural Language Processing (NLP); Personalized Learning Paths; Sentiment Analysis
This innovative study reimagines the role of Natural Language Processing (NLP) in individualized education by highlighting the critical need to incorporate cultural subtleties. While natural language processing (NLP) offers great potential for improving classroom instruction, current research frequently fails to account for the complex issues caused by cultural variation. This research fills a significant need by providing a novel framework for the detection and incorporation of cultural subtleties into individualized learning programs. Further research into common biases is driving the development of natural language processing models with greater cultural sensitivity and awareness, such as gender bias in Named Entity Recognition (NER) and sentiment bias in cultural preferences. In order to correct past biases and promote gender neutrality in educational content, the research makes use of an adaptive NER algorithm and a diverse training dataset. Similarly, to guarantee nuanced and fair sentiment evaluations, the study suggests regularly evaluating and retraining sentiment algorithms with datasets that represent multiple cultures. A Cultural Relevance Score of 0.9, Adaptive Content Embedding vectors [0.3, 0.6, -0.2.], and an impressive Cosine Similarity of 0.85 are some of the evaluation measures that highlight the effectiveness of the research. These measurements show encouraging gains, which confirms that the research might help make schools more welcoming and sensitive to different cultures. The research has the potential to revolutionize individualized education by making it more accessible and engagingfor students from all backgrounds. 2024 IEEE.
Chauhan M.; Rahman H.; Ibrahim U.M.M.; Reddy D.S.; Padmanabhan A.S.; Thirumalaikumari T.; Babu G.C.
International Conference on E-Mobility, Power Control and Smart Systems: Futuristic Technologies for Sustainable Solutions, ICEMPS 2024
Chauhan M., Symbiosis Law School-NOIDA, India, Symbiosis International (Deemed University), Pune, India; Rahman H., Malang, Indonesia; Ibrahim U.M.M., Malang, Indonesia; Reddy D.S., Vardhaman College of Engineering, Department of Mathematics, Telangana, Hyderabad, India; Padmanabhan A.S., Law Christ University, Bangalore, India; Thirumalaikumari T., Saveetha College of Liberal Arts and Sciences, Chennai, India; Babu G.C., Gokaraju Rangaraju Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India
File Validation intheData Ingestion Process Using Apache NiFi
Apache NiFi; Custom processor; Data ingestion; File validation; Frequency validation
In the industries of today, development and maintenance of data pipelines is of paramount importance. With large volumes of data being generated across industries on a continuous basis, there is a growing need to process and store this ingested data in a fast, and efficient manner. Apache NiFi is one such tool which possesses crucial capabilities that can be used to enhance, modify, and automate data pipelines. However, automation of the ingestion process creates certain inherent issues which, without being resolved, tend to be detrimental to the entire ingestion process. These issues vary in nature, ranging from corrupted data to changes in the file schema, to name a few. In this paper, a solution to this problem is proposed. By exploiting Apache NiFis custom processor development capabilities, problem-specific processors can be designed and deployed which can ensure accurate validation of the ingestion process on a real-time basis. To demonstrate this, two processors were developed as a proof-of-concept, which tackle specific file-related validation issues in the ingestion processthat of the file size, and, the ingestion frequency. These custom-built processors are designed to be inserted into the pipeline at key points to ensure that the ingested data is validated against certain standards and requirements. Having successfully demonstrated its capabilities, the paper presents the exploitation of Apache NiFis custom processor capabilities as a potential way forward to resolve the plethora of ingestion issues in industry, today. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 299-310.
Irfan M., CHRIST (Deemed to be University), Bangalore, 560029, India; Gangadhar A., Binghamton University, State University of New York, Binghamton, 13902, NY, United States; George J., CHRIST (Deemed to be University), Bangalore, 560029, India
Domain-Driven Summarization: Models for Diverse Content Realms
Automatic text summarization; Computational resource optimization; Domain-specific enhancements; Domain-specific optimizations; Natural language processing domain-agnostic summarization; Summarization techniques
In todays information-rich landscape, automatic text summarization systems are pivotal in condensing extensive textual content into concise and informative summaries. The current study ventures into domain-agnostic summarization, delving into advanced models spanning various domains, such as business, entertainment, sports, politics, and technology. The study aims to uncover domain-specific enhancements, assess resource efficiency, and explore the boundaries of applicability. This study covers nine cutting-edge models, including Google Pegasus-Large, Facebook BART-Base, SSHLEIFER DistilBART-CNN-6-6, Facebook BART-Large, T5-Large, T5-Base, Facebook BART-Large-CNN, Facebook BART-Large-Xsum, and SSHLEIFER DistilBART-Xsum-12-1. Each model undergoes rigorous evaluation, revealing its efficacy within various domains. Google Pegasus-Large emerges as a standout choice for cross-domain summarization, while Facebook BART-Base demonstrates remarkable stability. Models like SSHLEIFER DistilBART-CNN-6-6, T5 variants, and others contribute to the evolving landscape of summarization. This study endeavors to establish a robust foundation for enhancing the efficiency and effectiveness of summarization techniques within various domains, thereby contributing valuable insights to the broader literature on text summarization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 291-298.
Prince D., Christ (Deemed to be University), Bangalore, India; Jacob L., Christ (Deemed to be University), Bangalore, India; Reddy K.B., Christ (Deemed to be University), Bangalore, India
A Comprehensive Review of Linear Regression, Random Forest, XGBoost, and SVR: Integrating Machine Learning and Actuarial Science for Health Insurance Pricing
Actuarial science; Artificial intelligence; Data science; Machine learning; Supervised learning
Actuarial science and data science are being studied as a fusion using Industry 4.0 technologies such as the Internet of Things, artificial intelligence, big data, and machine learning (ML) algorithms. When analyzing earlier components of actuarial science, it could have been more accurate and quick, but when later stages of AI and ML were integrated, the algorithms weren't up to the standard, and actuaries experienced some accuracy concerns. The company requires actuaries to be precise with analysis to acquire reliable results. As a result of the large amount of data these companies collect, a choice made manually may turn out to be incorrect. We will, therefore, examine alternative models in this article as part of the decision-making process. Once we have chosen the best path of action, we will use our actuarial expertise to evaluate the risk associated with specific charges features. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 355-367.
Agarwal V., Christ (Deemed to be University), Bangalore, India; Singh M., Christ (Deemed to be University), Bangalore, India; Kumar K.P., Christ (Deemed to be University), Bangalore, India
Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches
AI (Artificial Intelligence); cyber security; cyber threats; threat detection
The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain. 2024 IEEE.
Duary S.; Choudhury P.; Mishra S.; Sharma V.; Rao D.D.; Paul Aderemi A.
4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024
Duary S., Kalinga Institute of Industrial Technology, India; Choudhury P., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Rao D.D., Infosys, Independent Researcher Consultant, United States; Paul Aderemi A., University of Bolton, School of Creative Technologies, United Kingdom
Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model
Liu estimator; Mean squared error; Multicollinearity; Ridge estimator; Two-parameter logistic regression
The logistic regression model is an invaluable tool for predicting binary response variables, yet it faces a significant challenge in scenarios where explanatory variables exhibit multicollinearity. Multicollinearity hinders the models ability to provide accurate and reliable predictions. To address this critical issue, this study introduces innovative combinations of Ridge and Liu estimators tailored for the two-parameter logistic regression model. To evaluate the effectiveness of the combination of ridge and Liu estimators under the two-parameter logistic regression, a real-world dataset from the medical domain is utilized, and Mean Squared Errors are employed as a performance metric. The findings of our investigation revealed that the ridge estimator, denoted as k4, outperforms other Liu estimators when multicollinearity is present in the data. The significance of this research lies in its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and researchers alike. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 195-202.
Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Chakraborty A., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India
Improved Acceptance model: Unblocking Potential of Blockchain in Banking Space
Decentralization; immutability and cryptocurrencies; ledger; Smart contracts
Over the past ten years, blockchain has emerged as the new buzzword in the banking sector.The new technology is being adopted globally in many industries, including the business sector,because of its unique uses and features. However, no adoption model is available to help with this process.This research paper examines the new technology known as blockchain, which powers cryptocurrencies like Bitcoin and others. It looks at what blockchain technology is, how it works especially in the banking sector, and how it can change and upend the financial services sector. It outlines the features of the technology and discusses why these can have a significant effect on the financial industry as a whole in areas like identity services, payments, and settlements in addition to spawning new products based on things like 'smart contracts'. The adoption variables found in the literature study were used to gather, test, and evaluate the official papers that are currently available from regulatory organizations, practitioners, and research bodies. This study was able to classify adoption factors into three categories - supporting, impeding, and circumstantial - identify a new adoption factor, and determine the relative relevance of the factors. Consequently, an institutional adoption paradigm for blockchain technology in the banking sector is put out. In light of this, it is advised to conduct additional research on using the suggested model at banks using the new technology in order to assess its suitability. 2024 IEEE.
Kumar A.; Mallik S.; Mishra S.; Sharma V.; Hewage P.; Iwendi C.
Kumar A., Kalinga Institute of Industrial Technology, India; Mallik S., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Hewage P., University of Bolton, School of Creative Technologies, United Kingdom; Iwendi C., University of Bolton, School of Creative Technologies, United Kingdom
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Artificial intelligence; Deep learning; Facial recognition
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Kesarwani T.; Mittal R.; Panwar D.; Saini G.L.; Kumar S.
Lecture Notes in Networks and Systems, Vol-969 LNNS, pp. 457-467.
Kesarwani T., Manipal University Jaipur, Rajasthan, Jaipur, India; Mittal R., Manipal University Jaipur, Rajasthan, Jaipur, India; Panwar D., Manipal University Jaipur, Rajasthan, Jaipur, India; Saini G.L., Manipal University Jaipur, Rajasthan, Jaipur, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bangalore, 560074, India
From Text to Action: NLP Techniques for Washing Machine Manual Processing
BERT; Extraction; Hugging Face; Ontology; Q/A Pipeline; Segmentation of text; Text Summarization; Translation
This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals. 2024 Elsevier B.V.. All rights reserved.
Biju V.G.; Babu B.; Asghar A.; Prathap B.R.; Reddy V.
Procedia Computer Science, Vol-235, pp. 1903-1919.
https://doi.org/10.1016/j.procs.2024.04.181 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196366834&doi=10.1016%2fj.procs.2024.04.181&partnerID=40&md5=3530a948cdb5c695e7d8189553ac0744
Biju V.G., THWS, Germany, Germany; Babu B., THWS, Germany, Germany; Asghar A., THWS, Germany, Germany; Prathap B.R., CHRIST Deemed to Be University, Bangalore, India; Reddy V., CHRIST Deemed to Be University, Bangalore, India
Insights on the Optical and Infrared Nature of MAXI J0709-159: Implications for High-Mass X-ray Binaries
Circumstellar disks; Classical Be stars; HMXB; Photometry
In our previous study (Bhattacharyya et al., 2022), HD 54786, the optical counterpart of the MAXI J0709-159 system, was identified to be an evolved star, departing from the main sequence, based on comparisons with non-X-ray binary systems. In this paper, using color-magnitude diagram (CMD) analysis for High-Mass X-ray Binaries (HMXBs) and statistical t-tests, we found evidence supporting HD 54786s potential membership in both Be/X-ray binaries (BeXRBs) and supergiant X-ray binaries (SgXBs) populations of HMXBs. Hence, our study points towards dual optical characteristics of HD 54786, as an X-ray binary star and also belonging to a distinct evolutionary phase from BeXRB towards SgXB. Our further analysis suggests that MAXI J0709-159, associated with HD 54786, exhibits low-level activity during the current epoch and possesses a limited amount of circumstellar material. Although similarities with the previously studied BeXRB system LSI +61? 235 (Coe et al., 1994) are noted, continued monitoring and data collection are essential to fully comprehend the complexities of MAXI J0709-159 and its evolutionary trajectory within the realm of HMXBs. 2024 Societe Royale des Sciences de Liege. All rights reserved.
Bhattacharyya S.; Mathew B.; Banerjee G.; Sindhu G.; Muneer S.; Kumar S.P.; Joshi S.
Bulletin de la Societe Royale des Sciences de Liege, Vol-93, No. 2, pp. 636-647.
Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, India; Banerjee G., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, India; Sindhu G., Department of Physics, St. Thomas College, Kerala, Kozhencherry, 689641, India; Muneer S., Indian Institute of Astrophysics, Koramangala, Bangalore, India; Kumar S.P., Indian Institute of Astrophysics, Koramangala, Bangalore, India; Joshi S., Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital, India
GLANCEGuided Language Through Autoregression Establishing Natural and Classifier-Free Editing
Autoregression; Classifier-free guidance; Diffusion models; Generative adversarial networks; Guided diffusion; Image inpainting; Image tokenization; Image-to-Image generation; Text-to-image generation
In this study, researchers aimed to simplify text conversion into images using the latest text-to-image generation methods. While these methods have improved the quality and relevance of generated images, certain crucial questions remained unanswered, limiting their practicality and overall quality. To address these issues, the researchers introduced a novel text-to-image method. This method allows for better control of the scene depicted in the image through text, enhances the tokenization process by incorporating specific knowledge about key image regions such as faces and important objects, and provides guidance to the transformer model without needing a classifier. The outcome of this work was a model that achieved state-of-the-art results in terms of image quality and human evaluation, enabling the generation of high-fidelity 512?512-pixel images. Moreover, this method introduced new capabilities, including scene editing, text editing with reference scenes, handling out-of-distribution text prompts, and generating story illustrations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 93-102.
Joshi D., Christ University, Bangalore, Indore, India; Tank A., Christ University, Bangalore, Indore, India; Pramila R.M., Christ University, Bangalore, Indore, India
Study of the Balmer Decrements for Galactic Classical Be Stars Using the Himalayan Chandra Telescope of India
Balmer decrement; Be star; emission lines; spectroscopy; variability
In a recent study, Banerjee et al. (2021) produced an atlas of all major emission lines found in a large sample of 115 Galactic field Be stars using the 2-m Himalayan Chandra Telescope (HCT) facility located at Ladakh, India. This paper presents our further exploration of these stars to estimate the electron density in their discs. Our study using Balmer decrement values indicate that their discs are generally optically thick in nature with electron density (ne) in their circumstellar envelopes (CEs) being in excess of 1013 cm-3 for around 65% of the stars. For another 19% stars, the average ne in their discs probably range between 1012 cm-3 and 1013 cm-3. We noticed that the nature of the H? and H? line profiles might not influence the observed Balmer decrement values (i.e. D34 and D54) of the sample of stars. Interestingly, we also found that around 50% of the Be stars displaying D34 greater than 2.7 are of earlier spectral types, i.e. within B0B3. 2024 Societe Royale des Sciences de Liege. All rights reserved.
Banerjee G.; Mathew B.; Bhattacharyya S.; Devaraj A.; Kartha S.; Joshi S.
Bulletin de la Societe Royale des Sciences de Liege, Vol-93, No. 2, pp. 504-513.
Banerjee G., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bangalore, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bangalore, India; Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bangalore, India; Devaraj A., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bangalore, India; Kartha S., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Main Road, Bangalore, India; Joshi S., Aryabhatta Research Institute of Observational Sciences, Nanital, India
Traffic Optimization and Route Detection Based on Air Quality and Pollution Level
AQI Monitoring; Eco-friendly Transportation; Environmental Data Integration; IoT; Route Detection; Sustainable Transportation; Urban Mobility
This research outlines the development of a groundbreaking Traffic Optimization and Route Detection system based on pollution and air quality. Urbanization has led to increased vehicular traffic, exacerbating concerns about air pollution and its adverse effects on public health. The proposed system aims to address this critical issue by integrating real-time environmental data into route recommendations, prioritizing routes that minimize exposure to high-pollution areas. Beyond improving air quality, the system promotes the health and well-being of commuters, encourages the adoption of eco-friendly transportation modes, and contributes to overall environmental sustainability. An air quality detection system is developed to gather data for the development of the system. This innovative approach aligns with the goals of efficient urban mobility, sustainable transportation, and data-driven decision-making. Through this research, we anticipate providing valuable insights into the potential impact of integrating pollution and air quality considerations into urban transportation systems, ultimately contributing to healthier and more sustainable urban environments. 2024 IEEE.
Sarkar B.; Mukherjee D.; Mukherjee J.; Sinha S.
Proceedings - 2024 13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024, pp. 1203-1208.
Sarkar B., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Mukherjee D., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Mukherjee J., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., Christ (Deemed to Be University), Department of Computer Science, Bangalore, India
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
Adaptive Learning Platforms (ALPs); Education; Intelligence; Personalized Learning Paths; Student Engagement
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE.
Dutta S.; Ranjan S.; Mishra S.; Sharma V.; Hewage P.; Iwendi C.
Dutta S., Kalinga Institute of Industrial Technology, India; Ranjan S., Kalinga Institute of Industrial Technology, India; Mishra S., Kalinga Institute of Industrial Technology, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Hewage P., University of Bolton, School of Creative Technologies, United Kingdom; Iwendi C., University of Bolton, School of Creative Technologies, United Kingdom
Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
DHH; feature engineering clustering; k-means; personalization
As e-Learning emerges as a promising tool for instruction delivery, personalizing the e-Learning platform for DHH learners will benefit them to improve their learning engagement and educational attainment. This study aims to collect and analyze the different features unique to DHH learners and analyze the significant features among them. This study highlights the importance of addressing the diversity among DHH learners, while creating a personalized learning environment for them. With this focus, we employ the K-Means clustering algorithm to group the learners based on similar needs and preferences and identified that distinguishing clusters can be formed within the DHH group. We also tried to understand the significant features contributing to forming well separated groups. These results provide valuable insights into the diverse preferences and requirements when they interact with the learning materials. These findings emphasize the significance of personalized approach for DHH learners in educational settings and serve as the stepping stone to develop a personalized learning environment for them. 2024 IEEE.
Poly A.; Banu P.K.N.; Azar A.T.; Kamal N.A.
2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024
Poly A., Christ (Deemed To Be University), Department of Computer Science, Bangalore, India; Banu P.K.N., Christ (Deemed To Be University), Department of Computer Science, Bangalore, India; Azar A.T., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Soft Computing Lab (ASSCL), Riyadh, Saudi Arabia, Benha University, Faculty of Computers and Artificial Intelligence, Benha, Egypt; Kamal N.A., Cairo University, Faculty of Engineering, Giza, Egypt
Big data; Data; Data analytics; Data economy; Data monetization; Data sources; Data valuation; Digital ecosystem; Digital transformation
The article explores the concept of data economy, which is based on the sharing of data across platforms and ecosystems. Data has evolved from factual information to a new asset for companies worldwide, and the article discusses its evolution from brittle paper records to complex databases and algorithms like blockchain. With a prediction of a data explosion of about 175 zettabytes by 2025, data is used extensively in various domains, from agriculture to healthcare. The article also discusses how the data economy is not domain-specific but is a universal shift as all companies transition to become technology-driven companies. The data network effect is a cycle that uses data to acquire service users and generate more data. This has become a B2B service model that has added profits to various tech giants balance sheets. The article concludes by exploring the current need for data sharing across organizations and the future scope of the data economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Shukla S.; Elsheba D.; Chopra G.; Bisht K.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 517-527.
Shukla S., Christ University, Bangalore, India; Elsheba D., Christ University, Bangalore, India; Chopra G., Christ University, Bangalore, India; Bisht K., Christ University, Bangalore, India
Role of Artificial Intelligence and Robotics in Shaping the Students: A Higher Educational Perspective
and Students; Artificial intelligence; Higher education; Robotics
An unprecedented shift in technology has begun in the modern era. Robotics and artificial intelligence (AI) advancements have created fresh positions while de-skilling or retraining many existing ones. Technical developments at higher education institutions (HEIs) protect students against potential changes in their field of study brought on by A) and prepare them for success in the workplace. This research aims to investigate how, over the past 150 years; globalization has fundamentally changed human civilization. Conventional education confronts enormous challenges as energy, the internet of things, and the cyber-physical systems they oversee diminish. One may argue that energy, the internet of things, and the cyber-physical systems that are under its jurisdiction are the foundations of all future education. The demise of these systems presents a significant threat to traditional schooling. Students' screen time is increased by this action, which has an impact on their mental health. Five-fold cross-validation with 210 students from Delhi NCR and abroad is beneficial for the classification techniques SVM, Naive Bayes, and Random Forest. The study examined the factors that contributed to an increased rate of mental health issues among undergraduate students in Delhi, India, following the introduction of the COVID-19 virus. The results have demonstrated that while technology's practical applications will likely have a positive influence on education in the future, there may be negative effects as well. This is an opportunity for educators and learners to support excellence and remove obstacles that prevent many kids and schools from achieving it. Therefore, in the future, every nation will need to create an education system that is more technologically sophisticated. 2024 IEEE.
Dornadula V.H.R.; Kamatchi A.; Manichander T.; Mehra S.; Pathak P.; Joseph C.
Dornadula V.H.R., Training and R&d, Stnup Private Limited, Andhra Pradesh, Tirupati, India; Kamatchi A., Srm Institute of Science and Technology, Department of Corporate Secretaryship and Accounting and Finanace, Tamilnadu,Katankulathur, Chennai, India; Manichander T., Assam University (A Central University), Department of Education, Assam, Silchar, India; Mehra S., Lovely Professional University, School of Design (Multimedia), India; Pathak P., Auro University, School of Liberal Arts and Human Sciences, Surat, India; Joseph C., Christ (Deemed to Be University), School of Law, India
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Artificial neural network; K-Nearest neighbors; Logistic regression; Machine learning; Naive bayes; Natural language processing; Random forest; Sentiment analysis; Supervised
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 257-267.
Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India