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Lightweight Cryptography Algorithms for Internet of Things enabled Networks: An Overview

Journal of Physics: Conference Series 2021年第1期1717卷

作者： L.Mary Shamala Dr.G. Zayaraz Dr.K. Vivekanandan Dr.V. Vijayalakshmi Research Scholar Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India Professor Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India Associate Professor Department of Electronics and Communication Engineering Pondicherry Engineering College Puducherry India

Internet of Things is a worldwide set-up of interconnected entities that permits millions of devices to communicate with each other. Combined with reliable communication, ensuring security concerning confidentiality, integrity, and authenticity is a great challenge in IoT. Unsecured IoT devices open gateway for attacks. Unprotected and vulnerable devices, at times, allow easy entry for hackers, enabling them to have access to the shared network and personal, corporate assets. Conventional security measures are not suitable and cannot be applied to IoT technologies because of their minimum storage, low processing capacity, and limited computing power. Besides, scalability and heterogeneity issues arise when a variety of devices are interconnected. This paper presents the security threats and requirements of IoT cryptography, technology, and trends. The paper also discusses the challenges faced and the comparison of solutions already existing in IoT security.

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Detection of Tuberculosis based on Deep Learning based methods

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

作者： Murali Krishna Puttagunta S. Ravi Research Scholar Department of Computer Science School of Engineering and Technology Pondicherry University India Associate Professor Department of Computer Science School of Engineering and Technology Pondicherry University India

Pulmonary Tuberculosis (TB) one of the transmissible diseases, which is one of the top ten causes of death worldwide. The need to strengthen the treatment and screening in TB affected countries. In this paper, a systematic review is carried on deep learning-based computer-aided diagnostic (CAD) systems that are used to analyze chest X-rays for diagnosing pulmonary tuberculosis (TB). Deep learning has recently become one of the most successful techniques, particularly in the analysis of medical images. In Deep learning Convolutional Neural Networks (CNNs) are widely used for TB detection. A CNN model is commonly comprised of convolutional layers, sub-sampling / pooling layers, and fully connected layers. This paper also presents a comprehensive survey on the CNN models for the detection of TB. The progression of computer-aided diagnostic (CAD) systems has sped up the early diagnosis of TB.

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Identification of Phishing Urls Using Machine Learning

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： C Vineeth Krishna C Narayana Swamy A Viji Amutha Mary Mercy Paul Selvan UG Student Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Associate Professor Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Phishing is a typical assault on unsuspecting individuals by making them to reveal their one-of-a-kind data utilizing fake sites. The target of phishing site URLs is to purloin the individual data like client name, passwords and web based financial exchanges. Phishers utilize the sites which are outwardly and semantically like those genuine sites. As innovation keeps on developing, phishing strategies began to advance quickly and this should be forestalled by utilizing against phishing systems to recognize phishing. AI is a useful asset used to endeavor against phishing assaults. We as a whole know bunches of assaults are happening continuously situation in light of phishing URLS. There is no programmed procedure has been set up so far Multiple assaults of phishing URLs has not yet coordinated. In the proposed framework finding the phishing assaults/URLs, the System will identify various phishing assaults in equal succession and caution the ordinary clients with respect to phishing URLs.

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Technology assisted farming: Implications of IoT and AI

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IOP Conference Series: Materials science and engineering 2021年第1期1022卷

作者： N Aggarwal D Singh Research Scholar Department of Computer Science & Engineering Deenbandhu Chhotu Ram University of Science & Technology Murthal Associate Professor Department of Computer Science & Engineering Deenbandhu Chhotu Ram University of Science & Technology Murthal

With the advancement of technologies, things became intelligent with the capabilities of self-communication between them. Internet of Things (IoT) connected daily household things to the Internet and make them able to make decisions like the human mind. Sensors collect the real atmospheric data and with the help of Artificial intelligence (AI) algorithms analysis of data takes place so that devices behave more smartly. The present article discusses how IoT revolutionized the agricultural community. According to the study, it is analyzed that 70% population is dependent on agriculture for their livelihood in India, but the status of agriculture is no more concealed from society. With the involvement of technology, it becomes easy to predict temperature, rainfall, humidity, the need for fertilizers, water requirements, etc. The introduction of modern agriculture techniques using IoT & AI is revolutionizing the traditional agriculture methodologies and are making farming a profitable venture also.

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Securing the MQTT Protocol using Enhanced Cryptographic techniques in IoT Surroundings

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

作者： M Sundarrajan A E Narayanan V Srithar Research Scholar Computer Science and Engineering Periyar Maniammai Institute of Science & Technology Thanjavur India. Associate Professor Computer Science and Engineering Periyar Maniammai Institute of Science & Technology Thanjavur India.

Protocol of MQTT - Message Queuing Telemetry Transport process is among the majority of lengthy procedures in IoT protocols. Nevertheless, this particular process doesn't apply a solid protection pattern by default, and that doesn't let a protected authentication mechanism between individuals within the reception. In addition, we can't believe in the confidentiality as well as integrity of information. Little IoT products deliver additional and much smarter details within aspects of IoT and so on. This will make the protection issues within the protocols applied to the IoT particularly crucial. The standard format of MQTT process highly suggests apply it through Transport Layer Security (TLS) rather than basic TCP. Nevertheless, this particular alternative isn't probable in many little products which form the IoT environment. In many cases, the constrained materials of IoT products stop the usage of protected asymmetric cryptography systems applied through itself. So, we suggest creating a safety scheme of MQTT process by means of Cryptographic techniques, for equally difficulties, the authentication schema and also the reliable information confidentiality & information integrity. We execute this particular protection schema without changing the conventional process emails. And lastly, we show a period benefits test utilizing a Java library.

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A hybrid machine learning strategy for aquatic plant surveillance in sustainable aqua-ecosystems using IoT attributes

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Aquaculture 2025年 609卷

作者： D. Palanikkumar T. Anuradha Jothi Ramalingam S.S. Sivaraju Department of Computer and Science Engineering Dr. NGP Institute of Technology Coimbatore 641048 Tamil Nadu India Department of Electrical and Electronics Engineering KCG College of Technology Chennai 600097 Tamil Nadu India Associate Professor Department of Mathematical and Computational Sciences National Institute of Technology Surathkal Mangalore 575 025 Karnataka India Department of Electrical and Electronics Engineering R V S College of Engineering and Technology Kannampalayam Coimbatore 641402 Tamil Nadu India

Using a hybridized machine learning framework combined with IoT technology, this research proposes a unique way of monitoring and maintaining the optimal condition of aquatic plants in ecosystems. Our method integrates state-of-the-art convolutional neural networks for plant health data analysis, time series analysis for water quality temporal variation interpretation, and clustering algorithms for ecological data pattern identification. We used an extensive open-source dataset that included various characteristics of IoT platforms, such as water temperature, pH, turbidity, dissolved oxygen levels, and particular metrics for plant development. Because of its critical role in maintaining ecological harmony in our research region, a certain aqua-plant is the primary focus of the dataset. Our Hybrid Machine Learning Strategy (HMLS) demonstrated outstanding performance in forecasting aquatic plant health and growth patterns with a Graph Neural Network (GNN), obtaining a 94 % accuracy rate in plant health data assessments and categorization. In addition, the contour-based clustering technique was employed to effectively group comparable ecological circumstances with a 93.5 % accuracy rate, while time series analysis identified temporal changes with a 94.22 % forecast accuracy. These results prove that our integrated strategy serves a purpose for aqua-ecosystem sustainability management by offering predicted insights and real-time monitoring. This study substantially contributes to environmental monitoring by using modern machine learning algorithms and IoT technologies to provide a scalable and economical solution for aquatic ecosystem conservation.

关键词： Accuracy Aqua parameters Clustering Contour Learning strategy Plant health Predictive mechanism Sensors Temporal

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Sentiment Analysis using Neural Network and LSTM

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： Akana Chandra Mouli Venkata Srinivas Ch. Satyanarayana Ch. Divakar Katikireddy Phani Sirisha Professor & Head Department of Computer Science AMC Engineering College Bengaluru India Professor Department of CSE & Registrar JNTUK Kakinada AP India Professor Department of CSE SRKR Engineering College Bhimavaram APIndia Associate Professor Department of Computer Science AMC Engineering College Bengaluru India

People put their opinions or views on various events happening in the society or world. Twitter is one of the best social networking sites where a huge amount of data generates on the daily basis. These data can be used to classify their tweets based on various sentiments attached to them. Numerous technologies are applied to analyse the sentiments of users. Sentiment analysis needs a very efficient method to manage long arrangement data and their drawn-out dependencies. In this paper, we have applied a deep learning technique to perform Twitter sentiment analysis. Simple Neural Network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) methods are applied for the sentiment analysis and their performances are evaluated. The LSTM is the best among all proposed techniques with the highest accuracy of 87%. We have collected a Twitter dataset from Kaggle to perform our experiment. The future improvement of the proposed research should include REST APIs and web crawling-based solutions to get live tweets to perform real-time analytics. We have analysed 1.6 million tweets in our research work.

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Mathematical Model Approach for AC and DC Regulators Excitation Systems (ES)

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： Diresie Yirgu Demeke Balachandra Pattanaik Syed Masood Hussain Abate Abibual Balanaidu Sundaram Lecturer Department of Mathematics Professor Department Electrical and Computer Engineering Faculty of Engineering and Technology Lecturer Department Electrical and Computer Engineering Faculty of Engineering and Technology Associate Professor Department of computer Science College of Informatics BLUEHORAUNIVERSITY Blue Hora Ethiopia Africa

Balancing equations for excitation system is very basic and fundamental concept and in some cases it becomes more difficult so that a mathematical treatment is needed in order to make it easy for AC and DC Regulators Excitation Systems (ES). This research paper mainly focuses on an excellent application of The power generating units and higher power motors are majority included by wound field synchronous machines because of it has flexible field excitation, flux intrinsic weakening capacity and high efficiency. It can be also used in low to medium power range for high end solutions in a wide range. This paper is analyzing a study of modern methods and technologies of excitation system for AC and DC regulators.

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Survey on Forecasting the vulnerability of Covid 19 in Tamil Nadu

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

作者： P. Ananthi S. Jabeen Begum V. Latha Jothi S. Kayalvili S. Gokulraj M.E student Computer Science and Engineering Velalar College of Engineering and Technology Erode Tamil Nadu India Professor Computer Science and Engineering Velalar College of Engineering and Technology Erode Tamil Nadu India Associate Professor Computer Science and Engineering Velalar College of Engineering and Technology Erode Tamil Nadu India

Predictive and analytic models for forecasting the vulnerability and recovery rate of patients who are affected by COVID 19 are made in this project for good analysis and better decision-making. In this project, linear regression (LR) a Machine Learning model is used to forecast the number of patients will get the infection in near future. By simulating SIRD model, the infection spread and recovery rate of the disease in a geographic region can be predicted. The vulnerability of the disease is checked by observing the transmission of disease over a period. In addition to this many info graphic models and graphs are created for easy understanding of data to get more insights about the disease. However, these prediction models enable us to make quick response of pandemic and to bring a conclusion to the disease. INDEX TERMS: Covid 19, data science, machine learning, prediction, analysis, pandemic, recovery and infection.

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Measuring Accuracy of Stock Price Prediction Using Machine Learning Based Classifiers

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IOP Conference Series: Materials science and engineering 2021年第1期1099卷

作者： Ranjeet Kaur Dr. Yogesh Kumar Sharma Devershi Pallavi Bhatt Research Scholar Department of Computer Science & Engineering Sri Jagdish Prasad Jhabarmal Tibrewala University Jhunjhunu India Associate Professor & Head Department of Computer Science & Engineering Shri Jagdish Prasad Jhabarmal Tibrewala University Jhunjhunu India Associate Professor & Head Department of Computer Applications Manipal University Jaipur India

Stock market prediction means to decide the future development of the stock estimation of a budgetary trade. The exact forecast of offer value development will prompt more benefit speculators can make. The investigation dependent on the past gathered enormous information with the utilization of the AI strategies is appropriate for different fields. The basic aim is to generate the analysis for driving good information which will be useful for the purpose of decision making. The quality of the decisions will definitely be enhanced. There are various machine learning techniques lies with different accuracies. The selection of the best technique such that the highest level of accuracy can be achieved. In the current research there are three techniques with different variant are tested for showing the relative accuracy of the specific technique. The all these techniques are based on supervised learning will requires training for the better accuracy. In current research paper all the techniques with different variants are trained with the different sizes of the training sets. These training sizes are 70:30,50:50 and 30:70. The best variant is the 70:30 for the KNN. The given variant shows the highest accuracy in terms of the prediction.

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