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Exploring nanotechnology for diagnostic, therapy and medicine

IOP Conference Series: Materials science and engineering 2021年第1期1091卷

作者： T. Chithrakumar M Thangamani S. Pon miruthu vasini Assistant Professor Computer Science and Engineering Sri Ramakrishna engineering college Coimbatore Associate Professor Information Technology Kongu Engineering College Tamilnadu India Student of Computer and Technology Kongu a Engineering College Tamilnadu

In current days, knowledge enhancement is growing more. Nanotechnology can applied to self-repeating machines and mechanical technology, compound designing, mechanical designing, science, natural designing, and electrical designing. Nanotechnology has the great preference to improves produce new and superior product. Nanomaterials to perceive avoid and eliminate pollution. Nanotechnologies are blueprint, invention and applied in medicine, therapy, diagnostic, sequencing and environmental protection. The usage of nanotechnology is rapidly increases in entire future market. This article explore the nanotechnology for medicine, diagnostic and therapy.

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Any Where Vote: ATM as Evm

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： A. Sangeerani Devi A. Chinnasamy D. Kavitha A. Shali Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India

The Election Commission has been using them anual system during elections to vote in the digitization of this modern age. In all Elections (Presidential\Parliamentary), electronic voting machines (EVMs) are be introduced, although they are not fully automated and have many limitations. And Moreover, every election is decided by those who show up. The main idea is to build a system that modifies the ATM's (Automated Teller Machine) system to work as EVM (Electronic Voting Machine) during the elections dates. This will allow to facilitate the election process for any voter in India to cast the vote to their respective constituency from anywhere in India by going to the nearest ATM in his/her location. When the voter inserts his/her ATM card, a voting option will be enabled for casting the vote. When the voter selects the voting option, they have to pass through a 2-level authentication procedure.. Then, the user is allowed to cast his vote by selecting a political candidate from a list of political parties of that constituency. Again, authentication will be asked before casting vote. Whenitiscompletedthecount in that unique hash value is incremented and the voting access to the user will be disabled for the particular voter. All votes are stored directly in the Electoral Commission main database. Kerberos Authentication Server and Voting Card Database is used for voting and authentication process.

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Safety Monitoring System in Minimg Environment Using Iot

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： D. Kavitha A. Chinnasamy A. Sangeerani Devi A. Shali Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India

The average mine worker is exposed to the harsh underground environment which can sometimes cause an injury or loss of life. The older methods of mine condition monitoring involved using a person to go down and report back. This method is however dangerous as the person who is monitoring a specific hazard could be harmed by that hazard. This standard can be reinforced through the implementation of modern technology which is central to the non-invasive mine safety system with wireless sensor networks to obtain the best possible result.

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A Comparative Analysis of using Various Machine learning Techniques based on Drowsy Driver Detection

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IOP Conference Series: Materials science and engineering 2021年第1期1119卷

作者： A Bano A Saxena G K Das Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Assistant Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India

In image processing or computer vision, image segmentation is a vital issue for applications such as scene understanding, medical image evaluation, robotic perception, video surveillance, increased reality or compression, etc. Every year in road accidents caused because of human mistake, the numbers of dead and injured are rising. Drowsiness and driving are particularly risky and difficult to recognize. The second leading cause of road crashes in drowsiness after alcohol. Detecting driver drowsiness is a technology of safety for vehicles that helps placed an end to driver injuries that are dozy. One of the main causes of road accidents is driver drowsiness. It is a very serious issue for road safety. We have presented various methods for detecting the drowsiness of the driverin this paper and the comparisons among such methods are extremely challenging. For this purpose, we have compared machine learning methods based on facial expression, especially on eye state. Apart from eye detection, it performed experiments on mouth detection and face detection as well. This paper explores several methods for machine learning, like SVM, CNN, or HMM. From the analysis, we have found that the HMM model achieved more accurate results in comparisonto others.

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Smart public transportation: A future framework for sustainable new cities (Case study Greater Cairo)

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IOP Conference Series: Earth and Environmental science 2022年第1期992卷

作者： Hayam M Omayer Assistant Professor Architecture Department Cairo Higher Institute for Engineering Computer Science and Management No.2 Banafseg Center2 first Settlement Cairo Postcode 11477 Egypt.

In the context of the tremendous progress in information technology in all spheres of life, it is necessary to adopt more intelligence solutions to increase the quality of life and achieve sustainable cities. Currently, without a doubt, we have the ability to make all city services accessible to everybody. Transportation and mobility systems are critical components of sustainable city development. Therefore, the scientific literature on the technological systems employed in transportation systems and their primary uses is extensive, outlining their application to improving people's quality of life through promoting a sustainable environment. Recently. There is a new generation of ITS (intelligence transportation system) resulting from the encouragement of related public policy that has been deeply integrated with new infrastructure and new technologies for improving the quality of life. The new generation of ITS technology and urban analysis and planning technology can not only meet the current problems of the coordinated development of the public transportation system and big cities like Cairo, but also meet the needs of the city's future development in advance. This paper will discuss the transformation of intelligent public transportation, the opportunities, and applications, using technologies, and implementation in various urban areas using a case study of China's smart transportation. Additionally, propose a future framework for sustainable transportation in new cities and the feasibility of implementing them in Egypt's new cities. Highlight the actions and outcomes of integrating technologies, as well as their environmental, urban, economic, and social impacts. In order to develop and increase the quality of life in the cities of Egypt and the developing world in order to achieve sustainability goals.

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Agricultural Farming Survey Using Iot

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： Mrs. A. Shali Mrs. A. Sangeerani Devi Mrs. D. Kavitha Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India

Web of things (IoT) is a promising innovation which gives productive and solid arrangements towards the modernization of a few spaces. IoT based arrangements are being created to naturally keep up and screen rural homesteads with negligible human association. The article presents numerous parts of advancements associated with the space of IoT in farming. It clarifies the significant segments of IoT based keen cultivating. A thorough conversation on network advances utilized in IoT based horticulture has been introduced, that includes network design and layers, network geographies utilized, and conventions. Moreover, the association of IoT based farming frameworks with pertinent advances including distributed computing, huge information stockpiling and examination has likewise been introduced. Likewise, security issues in IoT farming have been featured. A rundown of advanced mobile phone based and sensor based applications created for various parts of ranch the executives has likewise been introduced. Finally, the guidelines and approaches made by a few nations to normalize IoT based agribusiness have been introduced alongside barely any accessible examples of overcoming adversity.

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Efficient Feature Extraction from Multispectral Images for Face Recognition Applications: A Deep Learning Approach

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： R Sudharsanan PV Gopirajan K Suresh Kumar Assistant Professor Department of Computer Science and Engineering Karpaga Vinayaga College of Engineering and Technology GST road Padalam Chengalpattu Tamil Nadu - 603308 India Assistant Professor (SG) Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Associate Professor Department of Information Technology Saveetha Engineering College Tamil Nadu India

In recent years many face recognition algorithms were used for the identification and authentication of a person to a system. However, still, feature extraction from multispectral images was considered to be a challenging task. Feature extraction, including highlight location and portrayal, assumes a significant job in real-time security-based applications. In this paper, a novel Geometric Algebra-based Multivariate Regression Feature Extraction (GA-MVRFE) algorithm was proposed to extract features from a huge dataset stored in the cloud efficiently. This proposed algorithm works with the supreme expedient deep learning approach - Convolutional Neural Network (CNN) for image classification. CNN will automatically detect significant features from the multispectral images without any human intrusion from a huge database. Real-time images were captured with three different cameras and applied filters over the images and were created as a dataset. To show the competence of the proposed algorithm, an exclusively created dataset with a set of 14,400 image data was applied in the proposed and other existing algorithms, and their efficiency and robustness were noted. Providentially, GA-MVRFE produced better accuracy in 'Face Recognition' with a less time fraction compared with former algorithms. Obtained accuracy % for Geometric Algebra Oriented fast and Rotated Brief (GA-ORB), Geometric Algebra Fast Retina key-point Extraction Algorithm (GA-FREAK), Trilateral Smooth Filtering (TRSF), Cross Regression Multiple View Features extraction (CRMVF) and GA-MVRFE was 87.81, 83.23, 90.72, 91.67 and 97.57 respectively.

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Machine learning based calculation of refractive index of polyethylene glycol polymer

Results in Chemistry

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Results in Chemistry 2025年 16卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Hardik Doshi H.S. Shreenidhi Manoranjan Parhi Devendra Singh Prabhjot Singh Bilakshan Purohit Kamal Kant Joshi Ahmad Abumalek Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi Universitly Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India Department of Computer science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 Uttarakhand India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Allied Science Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering Balkh University Balkh Afghanistan

This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. A databank comprising 99 experimental measurements were utilized to train and assess multiple algorithms, including Extra Trees, Random Forests, Decision Trees, Ensemble Learning, AdaBoost, K-Nearest Neighbors, Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Linear Regression, and Elastic Net. The dataset demonstrated a strong correlation between molecular weight and refractive index ( r = 0.64), and an inverse correlation with temperature ( r = −0.72). Model performance was assessed using R 2 , MSE, and AARE%, revealing that MLP-ANN, Linear Regression, and Elastic Net models yielded the highest accuracy on the testing dataset. Furthermore, SHAP analysis identified molecular weight as the dominant factor influencing refractive index predictions. The proposed approach significantly reduces the need for experimental measurements and offers a reliable, time-efficient solution for estimating the optical behavior of PEG polymers under varying conditions.

关键词： Machine learning Artificial intelligence Polyethylene glycol Refractive index Outlier detection SHAP analysis

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Skin Disease Prediction and Provision of Medical Advice Using Deep Learning

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： S. Hemavathi K. Jayasakthi Velmurugan Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India Associate Professor Department of Computer Science and Engineering Jeppiaar Engineering College Chennai-119 Tamil Nadu India

Skin diseases are hazardous and often contagious, especially melanoma, eczema, and impetigo. These skin diseases can be cured if detected early. The fundamental problem with it is, only an expert dermatologist is able to detect and classify such disease. Sometimes, the doctors also fail to correctly classify the disease and hence provide inappropriate medications to the patient. Our project proposes a skin disease detection method based on Image Processing and Deep Learning Techniques. Our system is Personal computer based so can be used even in remote areas. The patient needs to provide the image of the infected area and it is given as an input to the application. Image Processing and Deep Learning techniques process it and deliver the accurate output. The output is used to get the best medical cure for the disease with nearest hospital details. In this project, we present a comparison of two different approaches for real-time skin disease detection algorithm based on accuracy.

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Sensor integrated modules – Social distance & hygiene solutions for COVID-19 pandemic

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AIP Conference Proceedings 2024年第1期2512卷

作者： Kalyan Dusarlapudi Narasimha Raju Kuthuri Kattupalli Sudhakar Prathyusha Chennupati Shiny Syam Mallolu Sai Harshitha Padala Venkata Siva Kumari Narayanam Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Senior Assistant Professor Department of AI&DS Lakireddy Balireddy College of Engineering Mylavaram India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India Communication and soft skills department Koneru Lakshmaiah Education Foundation Vaddeswaram A.P India

The Covid-19 pandemic has opened doors to the academic community to explore more possibilities for innovations. The present paper discusses three prototypes developed for the user’s safety during the pandemic lifestyle. The modules offer alert and hygiene practices to helps users not to get affected by the Covid-19 Virus. The first module intended for user alerts to maintain social distance concerning nearest humans. The module prompts the buzzer if a person comes near within one-meter vicinity. The Second module is an automatic hand sanitiser dispenser for self-clean hands with disinfectant liquid. It uses IR sensors for hand proximity detection to trigger a pump to deliver sanitiser to the hands. The last module dispenses water into a glass. The sensors incorporated are ultrasound and IR sensors for glass detection, automatic water level and user water level control. These modules components, methodology and operation, are discussed. The cost-effective modules offer a long-standing performance with an Embedded board.

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