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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

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International Journal of computers and applications 2023年第1期45卷 36-50页

作者： Shukla, Alok Kumar Pippal, Sanjeev Kumar Chauhan, Sansar Singh Department of Computer Science and Engineering G.L. Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Genetic algorithms

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A Comparative Analysis on Various Modified Deep Convolution Neural Networks on Maize Plant Leaf Disease Classification 1

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Kumar, H. R. Sunil Poornima, K.M. Department of Computer Science and Engineering PES Institute of Technology and Management Shivamoggaa India Department of Computer Science and Engineering JNN College of Engineering Shivamoggaa India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good health is very important for maximum yield. Maize is one of the major food crops in the world and contributes major part in food security. There are various reasons for leaf to get diseases that include pathogens like virus, bacteria, pests and even adverse environmental conditions. The idea of this paper is to take data set of maize leaf images with disease like blight, common rust and gray leaf spot along with healthy leaves, train it on different modified deep convolutional neural networks (DCNNs) and use the trained models to test new leaf images. Deep convolutional neural networks have been modified as per the requirements by introducing new epochs, iterations, activation functions and learning rates. In the proposed work, ten different modified networks have been implemented starting from a simple AlexNet to a more complex network like EfficientNet-b0. In this experiment two different data set have been used, one from a standard data set from Kaggle and another is created manually by capturing images from our neighborhood farm lands at different stage of growth. Finally, a comparative study on different networks with two different data set is carried out. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Empowering Supply Chain Transparency and Risk management through Hybrid CNN and BiGRU Approach 1

Empowering Supply Chain Transparency and Risk Management thr...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Guhan, K. Lakshmi, S. Verma, Virendra Kumar Sai, M K Satya Sharma, Amit Kumar, B. Suresh Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Bharati Vidyapeeth Management Studies and Research Department of Management Business Administration Navi Mumbai India Symbiosis Institute of Business Management Bengaluru India Vignana Bharathi Institute of Technology Department of Mechanical Engineering Hyderabad India Lovely Professional University School of Computer Application Phagwara India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad India

ISBN: (纸本)9798350352931

Everything from needs to luxuries has been impacted by the COVID-19 pandemic in the manufacturing sector, and that includes the automotive industry. Thus, there is an immediate need for research into the automotive supply chain's sustainability, transparency, and competency development practices. For the automotive supply chains to successfully apply the Transparency for Sustainability (TfS) architecture in the long run, it also specifies a set of skills that are necessary. The three steps that comprise this proposed method are preprocessing, feature selection, and model training. The data had to be preprocessed in order for it to be ready for analysis. The feature selection procedure is comprised of the LDA topic model, term frequency, and correlation analysis. A GRU-DT was utilized for the model's training. The suggested method outperforms GRU and DT with an average accuracy of 96.29%. © 2024 IEEE.

关键词： Automotive industry

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Electroencephalography (EEG) Based Prediction of Learning and Responsiveness among Students 7

Electroencephalography (EEG) Based Prediction of Learning an...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Agrawal, Alok Kumar Ahmed, Bilal Jakhar, Yogesh Kumar Bajaj, Karan Kumar, Girish Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University India University of Engineering & Management Department of Computer Application Jaipur India

ISBN: (纸本)9798350340600

The constructivist method of teaching has been shown to have a major influence on the learning outcomes of students. In order to improve classroom instruction, it is essential to evaluate students' levels of involvement and attentiveness while they are being taught. Electroencephalography (EEG) can now be used to assess students' attentiveness in real time thanks to recent neuro-engineering breakthroughs and mobile brainwave sensors' availability. The purpose of this study is to provide an overview of EEG-based techniques with the intention of predicting constructivism and engagement among students of varying levels. The purpose of this study is to evaluate the efficacy of these approaches and to determine the extent to which they have the potential to enhance the design of future learning systems. This survey analyzes whether or not approaches based on EEG may be used to determine the level of constructivism and involvement displayed by pupils. The findings provide helpful insights that can be used to develop tailored teaching strategies and improve education's overall efficiency. However, some obstacles need to be overcome in order for EEG-based predictions to be accurate. Some of these obstacles include data quality and variability, individual differences, interpretability, generalization, and ethical considerations. © 2023 IEEE.

关键词： Forecasting

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Lifestyle Classification Amidst Psychological Impact of COVID: A Machine Learning Approach 7

Lifestyle Classification Amidst Psychological Impact of COVI...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

ISBN: (纸本)9798350340600

The COVID pandemic has caused significant social changes, affecting not just physical health but also individuals' psychological well-being. This study investigates the complex interaction between lifestyle choices and the psychological impact of the pandemic, using a Machine Learning (ML) technique to categorise and assess distinct lifestyle patterns in the context of COVID. In this study, a significant data from a variety of sources, including questionnaires, social media, and healthcare records, to get insight into how the pandemic has affected lifestyle characteristics such as exercise, food, social connections, work habits, and leisure activities. This study has analysed the psychological effects of these lifestyle changes, including stress, anxiety, sadness, and overall mental health. This research article implements the basic classification models like (Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, and XGBoost), which can classify various lifestyle changes among individuals based on their pandemic-related behaviours. The impact of these lifestyle changes on mental health outcomes is investigated, offering light on the intricate interplay between personal decisions and psychological well-being during times of crisis. © 2023 IEEE.

关键词： Machine learning

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R–attributes based rough graph concepts lattice

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14309-14319页

作者： Singh, Prem Kumar Department of Computer Science and Engineering Gandhi Institute of Technology and Management–Visakhapatnam Andhra Pradesh Visakhapatnam530045 India

The characterization of some important attributes in a given context is considered as one of the prominent issues for the decision making process. Same time another problem arises approximation of uncertainty and its graphical structure visualization. To resolve this issue, algebra of rough sets and its extensive properties is utilized in this paper. Extensively, current paper focuses on introducing R–Rough graph visualization of given context and its characterization in positive, negative and boundary regions based on user defined subset of granulation. The knowledge discovered from both of the proposed methods are also compared to validate the result. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Formal concept analysis

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A smart green mashrabiyya-shutter design for residential applications in Egypt

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HBRC Journal 2023年第1期19卷 229-252页

作者： Abdel Gelil Mohamed, Nermine Hamid Abd El-Rahman, Eslam Sadek, Mohamed Giza Egypt Mechatronics and Robotics Department Cairo Higher Institute for Engineering Computer Science and Management Cairo Egypt

The revival of traditional latticework mashrabiyya is continually urged in the Middle East and North Africa to address conflicting needs since it optimally balances environmental, social, and aesthetic functions. However, its material and craftsmanship costs and inflexibility are significant barriers. This study aims to provide a preliminary design for a green, smart mashrabiyya-like shutter responsive to climatic conditions and privacy needs in Egypt, manufactured with palm midribs, a waste material available in the MENA region due to annual trimming. The simple, yet responsive and traditional design outlined in this study has a considerable potential for residential applications not only in Egypt, but also in similar socio-environmental contexts. The design generation is based on three steps: 1) constructing a static model that maintains the basic socio-environmental needs, manufactured with palm midrib patterns, 2) modeling dynamic functions, using Grasshopper/Ladybug, through standardizing slats and deciding upon visibility and adjustable parts, and 3) integrating smart controls based on comfort indexes and indoor climatic readings. Using a simple circuit fitted with sensors, actuators, and a microcontroller, the design of a latticework shutter responsive to climate and privacy was generated. The concept depends on computing indoor weather data and comparing them to thermal, daylight, and wind comfort indexes. Consequently, automated decisions could be made to regulate the shutter parts in two ways: 1) its opening angle and 2) openness between the slats. Ultimately, the operating concept of the smart shutter in a room and automated flowcharts that make decisions in daytime and nighttime modes were proposed. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Visibility

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Evaluation of Water Quality Index Using Machine Learning Approach 4th

Evaluation of Water Quality Index Using Machine Learning App...

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Sen, Moupali Basu, Shreya Chatterjee, Arijit Banerjee, Anwesha Pal, Saheli Mukhopadhyay, Pritam Kumar Sahana, Sudipta Dutta, Stobak Department of Computer Science and Engineering Institute of Engineering & Management Kolkata University of Engineering & Management Kolkata Kolkata India Department of Computer Science and Technology Institute of Engineering & Management Kolkata University of Engineering & Management Kolkata Kolkata India

ISBN: (纸本)9789819927098

Clean water is the fundamental right of all people. The crisis for clean water is everywhere. So there has been an urgent need for clean water. Most people are not aware of clean water and are consuming dirty and infected water, and they suffer from different diseases. In our work, we attempt to analyze the water quality depending on various parameters like pH, temperature, conductivity, nitrate concentration, BOD, fecal coliform, and total coliform. Based on these parameters, our ML model will predict whether the water is clean and safe for consumption or not. To detect the index of the quality of water we have applied the concept of supervised ML techniques, and based on the range, we will be able to state the quality of water to be excellent or good or fair or poor or very poor or unfit for drinking. There has been a use of popular algorithms like support vector machine, K-nearest neighbors, Random Forest Classifier, and decision tree. Through this project, we wanted to create awareness among the people regarding the necessity of clean water. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Automatic Text Summarization using Text Rank Algorithm 3

Automatic Text Summarization using Text Rank Algorithm

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dumne, Rakhi Gavankar, Nitin L. Bokare, Madhav M. Waghmare, Vivek N. Walchand College of Engineering Department of Comp. Science and Engineering Sangli India Institute of Technology and Management Department of Computer Nanded India PVG' College of Engineering S.S. Dhamankar Institute of Management Department of Computer Engineering Nashik India

ISBN: (纸本)9798350354171

Automatic text summarization has been emerged as a valuable tool for quickly locating the significant information in vast text with minimal effort. The practice of constructing a summarized form of a text document that retains significant information and also withstand the overall meaning of the source text is known as text summarization. This study mainly concentrates on the extractive text summarization technique wherein the text summarization is carried out on single document as well as on multi document text. Further, the application is extended by implementing document summarization and URL summarization. Here, the sentence extraction method from the input text forms the basis of proposed text summarization technique. During sentence extraction, the weights are assigned to sentences which act as rank of these sentences, using page rank algorithm. In this study, extraction technique has been implemented to extract sentences having higher rank from the given input text. The study mainly focuses on obtaining high rank sentences from the given document in order to generate a high-quality summary of the input text. © 2024 IEEE.

关键词： Text mining

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