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2022 International Conference on Applied Research in engineering Sciences, ICARES 2022

作者： Nagi Reddy, K. M.subhan, C. Habeebullahassain, Syed Manu V.yalamaggad, C. Fakruddin, K. Department of Physics K.L.E.Society's S Nijalingappa College Karnataka Bengaluru India Lecturer in Physics Govt. Polytechnic Andhra Pradesh Madakasira India Department of Physics MES College of Arts Commerce and Science Karnataka Bengaluru India Centre for Soft Condensed Matter Sciences Karnataka Bengaluru India Department of Physics Ghousia College of Engineering Karnataka Ramanagaram India

This manuscript describes the determination of the orientational order parameter of the ferroelectric liquid crystals having chemical formulas C36H47NO5, C37H49NO5and C39H53NO5. The ordinary and extraordinary refractive indices of these compounds in the nematic phase are measured by using a small angle prism and modified spectrometer. By well-known Vuk's method the orientational order parameter is estimated. The optical textures of these compounds are apprehended using an optical polarizing microscope. The microscopic textures are analysed using MATLAB *** the orientational order parameter is estimated. The limitations and merits of the image analysis technique is discussed. © 2023 Elsevier Ltd. All rights reserved.

关键词： Refractive index

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Rasgulla—the ethnic Indian sweetmeat delicacy and its evolutionary journey through contemporary research

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Journal of Ethnic Foods 2021年第1期8卷 1-14页

作者： Sarkar, Tanmay Salauddin, Molla Chakraborty, Runu Department of Food Technology and Biochemical Engineering Faculty of Engineering and Technology Jadavpur University Jadavpur Kolkata 700032 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Malda 732 102 India Mirmadan Mohanlal Government Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Nadia 741 156 India

West Bengal and Odisha, two distinguished provinces of India, are consecrated with prosperous animal resources. Both territories have substantially affluent traditional dairy-based products. Rasgulla is one of such kind of traditional Indian dessert made from milk casein with attractive white colour having a spongy, porous structure and spherical shape, popular all over the world for its taste, flavour and unique texture. It is mainly originated in the West Bengal and Odisha, through a cascade of ethnic gastronomic phenomena. Both the traditional and cutting-edge practice of rasgulla preparation has its own impact on the sensory attributes of the product. Researchers’ approach to improve textural, colour and sensory qualities of this astonishing dairy product has an appulse on overall acceptability of the product. Different types of milk and coagulant have a tremendous effect on the final quality of the product in terms of nutritional, textural and palatability characteristics of rasgulla. To make this traditional sweetmeat more nutritious, fortification and enrichment of functional features have been studied. Anti-diabetic rasgulla has been prepared to conquer the diabetes mellitus through re-modulation in the extent of sugar used to process this sweetmeat. Shelf-life of casein based products is one of the main concerns for researchers, due to abundance of ample amount of nutrients for optimum growth of microorganisms, along with the warm and humid condition of Indian subcontinent which accelerates the microbial propagation. Though the product has immense nutritional and sensory idiosyncrasy as well as folk medicinal importance, it is yet to be explored in coetaneous medical sciences. © 2021, The Author(s).

关键词： Channa Dandi Ramayana Ethno-technology Reginoal cuisine Socio-religious food

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Kidnapped Vehicle Using Particle Filters-Self-Driving Car Nanodegree

Kidnapped Vehicle Using Particle Filters-Self-Driving Car Na...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Sunil Dhull Payal Thakur Yashwant Singh Sangwan Navjot Singh Talwandi Rajender Kumar Surender Kumar Panipat Institute of Engineering & Technology Dept of Computer Science Engineering Chandigarh University Punjab India Department of Computer Science Govt. College Hisar Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Guru Teg Bahadur College Bhawanigarh Sangrur Punjab India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Detecting and tracking stolen vehicles is an important task with self-driving cars. This study focuses on the use of particle filters to detect and track stolen vehicles. Particle filters are a probabilistic estimation method that can effectively handle the uncertainties and nonlinearities associated with vehicle tracking. The study examines the application of particle filters in the Nanodegree project to self-driving cars, specifically for the detection and tracking of stolen vehicles. The results demonstrate the efficiency of particle filters to accurately estimate the location and routes of hijacked vehicles, enabling effective recovery operations Vehicle occupied scenarios refer to situations in which a self-driving vehicle loses its location awareness The vehicle can measure its position observations based on a sensor. Using small- scale filters, this research aims to improve the ability of self- driving vehicles to reconstruct reality, accurately restoring its position in cases where they are unpredictable, ultimately contributing to the robustness and reliability of autonomous vehicle systems.

关键词： Uncertainty Accuracy Tracking Estimation Particle filters Robustness Public security

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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Entropy and Relative Entropy in the Composition of Commodities

Entropy and Relative Entropy in the Composition of Commoditi...

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International Conference on computer and Information Technology (ICCIT)

作者： Mahmudul Islam Rakib Mausumi Akter Sharmin Akter Milu Ashadun Nobi Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Information and Communication Technology Noakhali Govt. Mohila College Noakhali Bangladesh

This study uses statistical and information-theoretic measures to analyze the dynamics of the five top trading countries' positions in exporting eight commodities and their trade dependency on the GDP of corresponding importers from 1995 to 2017. Results from the Shannon entropy show that China has emerged as a trade giant in the 21st century, while the USA and Germany have dominated steadily. The relationship between trade and GDP is investigated from the relative entropy of VAR-predicted and empirical time series, revealing a higher divergence during financial crises. This implies relatively less market predictability during crises due to risk, reduced production and supply, and less market orientation of exporters. We observe that the USA and Germany are market-oriented exporters, China has become trade-reliant in this century, and Japan and France show relatively less production and supply. The Machinery and transport equipment market is found to be useful in identifying financial crises as it is more sensitive to financial risk and can be considered an important financial branch to tackle crises. The remarks of this research can be useful for traders and policymakers of the international trade market in portfolio investment and risk management.

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A Novel Mathematical Modelling Technique for Control of DC-DC Forward Converter

A Novel Mathematical Modelling Technique for Control of DC-D...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Prahlad Mundotiya Ankush Koli Neha Goyal Gayatri Kansotia Neetika Meena Harpal Tiwari Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Dept. of Electronics & Comm. Engg. Govt. R. C. Khaitan Polytechnic College Jaipur India

A DC-DC converter is a switched-mode circuitry that converts DC voltage according to the required load voltage. Conventional converters enable both voltage alleviation and voltage enhancement for the buck and boost converter respectively. This paper introduces a transformer-isolated forward converter, which produces the variable DC voltage as an output. A prominent converter demonstration strategy, the state-space averaging strategy, is addressed in detail for the forward converter. The state space technique provides the equivalent circuit models and the transfer functions as input-to-output voltage and control-to-output voltage. Simulation results show that the forward converter is better suited to achieve 6.4 volts from the given 24 volts as input voltage. Simultaneously, a control system is designed that evolves the characterization of the stability of the proposed converter.

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An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

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International Journal of Cognitive Computing in engineering 2021年 2卷 40-46页

作者： Kumari, Saloni Kumar, Deepika Mittal, Mamta Department of Electronics and Communication Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering Bharati Vidyapeeth's College of Engineering New Delhi India Department of Computer Science & Engineering G B Pant Govt. College of Engineering New Delhi 110020 India

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood. Machine learning algorithms help in identification and prediction of diabetes at an early stage. The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms. The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes. The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive Bayes for the classification. Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naïve Bayes, Bagging, GradientBoost, XGBoost, CatBoost. by taking accuracy, precision, recall, F1-score as the evaluation criteria. The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 79.04%, 73.48%, 71.45% and 80.6% respectively on the PIMA diabetes dataset. Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset. The proposed ensemble soft voting classifier has given 97.02% accuracy on the breast cancer dataset. © 2021

关键词： classification Diabetes ensemble Machine Learning soft voting

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Crowdsourcing of Internet of Things: Applications, Trends in technology and the Future

Crowdsourcing of Internet of Things: Applications, Trends in...

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International Conference on Power, Instrumentation, Control and Computing (PICC)

作者： Anoopa S Salim A Dhanya S Pankaj Department of Computer Science & Engineering College of Engineering Trivandrum Affiliated to APJ Abdulkalam Tecnological University Thiruvananthapuram Kerala India Department of Computer Science & Engineering Govt. Engineering College Thrissur Affiliated to APJ Abdulkalam Tecnological University Thiruvananthapuram Kerala India

Crowdsourcing of IoT devices is giving a new dimension to the Internet of things(IoT) applications. The crowd equipped with geolocated mobile devices enhances mobile crowd-sourcing which in turn contributes to new and unexplored use cases. This paper proposes a generalized architecture for the crowd-IoT system, enlightens a comprehensive survey of different applications of crowd-IoT, points out the importance of embedded intelligence in crowd-IoT systems, and suggests appropriate Artificial Intelligence(AI) tools and supporting frameworks for next-generation IoT applications. As crowd-IoT is an emerging research area, this paper aims to provide awareness and motivation to the innovations towards Crowdsourcing of IoT devices.

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Envisaging an Intelligent Blockchain Network by Intelligence Sharing

Envisaging an Intelligent Blockchain Network by Intelligence...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Nayak, Arijit De, Sourav Bhattacharyya, Siddhartha Mukhopadhyay, Debarka Muhammad, Khan Gorbachev, Sergey Alumnus Software Limited Kolkata India Cooch Behar Govt. Engineering College Dept. of Computer Science Engineering Cooch Behar India Rajnagar Mahavidyalaya Birbhum India Department of Computer Science and Engineering Bangalore India Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of National Research Tomsk State University Department of Innovative Technologies Tomsk Russia

ISBN: (纸本)9781665425773

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. © 2022 IEEE.

关键词： Blockchain

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A CNN-Based Framework with Focal Loss for Glaucoma Detection on Retinal Fundus Images

A CNN-Based Framework with Focal Loss for Glaucoma Detection...

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International Conference for Convergence of Technology (I2CT)

作者： Devika R G Sivakumar Ramachandran Linu Shine Jiji C V Department of ECE College of Engineering Trivandrum APJ Abdul Kalam Technological University Kerala India Department of ECE Govt. Engineering College Wayanad APJ Abdul Kalam Technological University Kerala India Department of Computer Science and Engineering Shiv Nadar University Chennai India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Optic cup-to-disc ratio (CDR) evaluation using ophthalmoscopy is the most widely used diagnostic method for detecting Glaucoma, a leading cause for blindness. CDR is often determined manually by ophthalmologists, giving rise to variability due to differences in expertise, experience, and different interpretations of subtle image features. This paper presents a novel methodology for the efficient detection of early glaucoma by harnessing image features within a precisely defined Region of Interest (ROI) centered around the optic disc. The method first segments the data to localize the optic disc using a pre-trained U-Net architecture. The generated optic disc mask is then used to detect the optic disc region in the color fundus images, which are then used to train the modified ResNet50 architecture for glaucoma screening. The proposed model was trained on the images acquired from the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge, conducted during ISBI 2022. The trained model is then tested on three publicly available datasets, namely ACRIMA, Drishti−GS, and RIM-ONE. The obtained results demonstrate the efficacy of our model to generalize to unseen data distributions, as evidenced by its exemplary performance on the three diverse datasets that were completely unseen during training. We also addressed the class imbalance problem in the training dataset by integrating the focal loss function into the proposed pipeline. Moreover, the proposed model has shown comparable performance with the other state-of-the-art techniques in identifying glaucomatous eyes from normal, healthy images.

关键词： Glaucoma Training Optical losses Solid modeling Atmospheric modeling Pipelines Optical imaging

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