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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Guria, Sudipa Banerjee, Sudarshan Bhattacharjee, Suddhasattwa Paul, Swarup Halder, Sobhan Das, Priyanka Institute of Engineering and Management Dept. of Computer Application Kolkata India

ISBN: (纸本)9798350348217

Due to the rise of recipe sharing websites, it is now quite easy to find online cooking instructions that list ingredients and cooking techniques. The development of recipe suggestion systems has also been a focus of several recipe sharing websites. Little has been done to analyse the relationship between cuisines and ingredients, while the majority of contemporary food-related research works concentrate on recipe suggestion systems. The work presented here offers an insight into which cuisines are the most related and what are a cuisine's important elements because we know that each cuisine is unique and has a separate list of ingredients. While the cuisine of food has been regarded as the class label, the ingredients have been seen as the features. In order to categorise the cuisine of unlabeled data, this work is focused on selecting the most important ingredients using various feature selection strategies and classification approaches. This has been done to investigate the fundamental connection between ingredients and cuisine. © 2023 IEEE.

关键词： Classification (of information)

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis 16

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arora, Rakhi Dixit, Nitin Sihare, Muskan Kinger, Shreshtha Shivhare, Sneha Kulshreshtha, Komal Institute of Technology & Management Computer Science and Engineering Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (纸本)9798331505264

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python. © 2024 IEEE.

关键词： Logistic regression

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Dynamic Approaches of Information Technology in the Betterment of Hospital 1

Dynamic Approaches of Information Technology in the Betterme...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Jadhav, Sharad Kadam, Manjushri Abhishek More, Seema Raju, B. Agarwal, Purvi Krishna Institute of Computer Application and Management Wathar India Department of Management Studies New Delhi India Jaywant College of Engineering and Poltechnic Maharashtra India Institute of Aeronautical Engineering Department of Freshman Engineering Hyderabad India Atharva College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350360707

Health care, both individual and institutional, is shaped by the digital revolution. Politicians and legislators are beginning to view the present models of care as unsustainable, which puts the healthcare industry at a critical crossroads. Nonetheless, the simultaneous application of blockchain, advanced IoT, AI, and smart sensors in Healthcare management System (HMS) remains fairly problematic. Some prominent and recurrent challenges are impeding the development of HMS, such as a lack of accurate and inexpensive "smart medical sensors", "heterogeneous wearable device networks", "non-standardized IoT system architectures", multidimensional data creation, and a high need for interoperability. This study gives a full overview of the deployment of these emerging technologies "Blockchain, IoT, AI, and Smart Sensors"in HMS to better understand the progress made thus far. Major enabling properties, significant use cases, as well as effective deployments are specifically investigated, as are current research and findings on the adoption of these emerging innovations in healthcare. © 2024 IEEE.

关键词： Information management

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An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

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Multimedia Tools and applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Registration of CT Images of Lung Using Fireworks Algorithm 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Nayak, Somen Mondal, Subhodip Obaid, Ahmed J. Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Mathematics Department of Computer Science University of Kufa Najaf Iraq

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

The acquisition of medical images from various modalities can provide a number of practical challenges that can be addressed through medical image registration. Intensity-based registration methods, which are used in multi-modal registration, merge multiple images with the same content into a single representation by applying image transformation. It is essential to edit photographs as best as you can. It is highly important to improve the similarity measure between the various photographs. We introduce a new method for registering lung’s CT image affected with COVID-19 in this work since the image registration process is fundamentally an improvement problem. Lung computed tomography image registration is a technique for aligning pre- and post-contrast images for analysis the effect of COVID-19 virus in the lung. A meta-heuristics-based optimization technique called FWA-IATF was explored for registering lung CT images. Following that, the FWA-IATF algorithm successfully registers the images. The results of registration based on the FWA-IATF method and PSO approach are compared. The results indicate that the FWA-IATF-based registration methods outperform the PSO-based registration approach while registering lung CT images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Sentiment Analysis for Movie Reviews on Social Media based on Sentiment Lexicon and Deep Learning 15

Sentiment Analysis for Movie Reviews on Social Media based o...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jakhar, Yogesh Kumar Mishra, Nidhi Department of Computer Application University of Engineering & Management Jaipur India Amity University Noida India

ISBN: (纸本)9798331300579

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User's review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Investigation of sustainable strategies with metaheuristic algorithm on surface roughness, cutting temperature, and chip morphology during machining of Ti6Al4V alloy

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International Journal of Machining and Machinability of Materials 2024年第3期26卷 223-243页

作者： Misra, Sutanu Kumar, Yogesh Paul, Goutam Pradhan, Buddhadeb Department of Mechanical Engineering University of Engineering and Management Kolkata India Department of Mechanical Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cutting-edge temperature, and chip morphology to understand the machinability of Ti-6Al-4V alloy using sustainable manufacturing techniques. Here, a metaheuristic algorithm approach has been introduced to determine the experimental data’s accuracy, which is approximately 75% for further investigation. Logistic regression and support vector machine provided higher accuracy and best fitted with the dataset and problem formulation. During this work, it has been observed that chip morphology is influenced by cutting parameters and lubrication strategy. The cryogenic plus MQL hybrid approach is the best method over cryogenic cooling by decreasing the heat by 12% and giving 8% better surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Titanium alloys

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A Comparison of Machine Learning Methods for Forecasting Solar Energy Production

A Comparison of Machine Learning Methods for Forecasting Sol...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Prajapati, Dharmistha K. Joshi, Ritesh A M Patel Institute of Computer Studies Ganpat University Master of Computer Application Mehsana India Prestige Institute of Engineering Management and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Fundamentally, the study's findings show how important machine learning algorithms are to improving solar power forecasts and system optimization. The study illustrates how well the ANN algorithm predicts solar energy generation by utilizing large datasets and advanced computational methods. This insight opens the door for proactive maintenance plans and increased PV installation reliability in addition to helping energy firms and decision-makers make well-informed judgments. Looking ahead, the conclusions of this study provide insightful advice for industrial solar energy forecasting procedures and further research. Through the identification of ideal model configurations and performance measures, interested parties can enhance their forecasting abilities and optimize the solar resource usage. Additionally, the incorporation of machine learning algorithms into real-time monitoring systems facilitates quick decisions, enabling communities to maximize energy production and adjust to shifting environmental conditions. The coming together of data-driven insights, technical innovation, and sustainability imperatives offers a once-in-a-lifetime chance to quicken the shift to renewable energy. We can fully utilize solar energy and lessen the negative consequences of climate change by utilizing machine learning and predictive analytics. In addition to fostering economic growth and energy security, this revolutionary change also builds a more resilient, just, and sustainable society for coming generations. In conclusion, a major advancement in the global search for clean and sustainable energy solutions is the use of machine learning algorithms into solar energy forecasts. We can take advantage of these developments and work together to create a more innovative and environmentally friendly future for everybody. © 2024 IEEE.

关键词： Clean energy

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Designing of Smart Pothole Detection System 9

Designing of Smart Pothole Detection System

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Maji, Anushka Sinha, Sagnik Banerjee, Subhabrata Das, Amit Kumar Gangopadhyay, Malay Rateria, Roshan Institute of Engineering & Management Department of Computer Science and Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Electronics & Communication Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350377972

Recently, Artificial Intelligence (AI) algorithms have been used to detect the potholes. In this article, a machine learning algorithm has been designed to solve the problem efficiently. A software model has also been created for drivers, enabling them to detect and recognize potholes in advance through a mobile application. This mobile app utilizes various sensors to detect potholes and provides timely alerts to the driver. Moreover, the system is capable of responding to newly detected potholes and alerts the driver about them. The proactive notification system improves road safety by ensuring drivers are informed about upcoming road hazards. © 2024 IEEE.

关键词： Mobile applications

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