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An Efficient Lightweight Cryptosystem Using Substitution-Shuffling Based Key Generation 26

An Efficient Lightweight Cryptosystem Using Substitution-Shu...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Provat, Al-Anher Haque, Md. Mokammel Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

In recent years, there has been a meteoric rise in the use of small computing devices, including RFID (Radio Frequency Identification) tags, wireless sensors, embedded electronics, and IoT (Internet of Things) gadgets. Given that these gadgets handle highly sensitive data, their protection should be a top priority. Therefore, it is crucial to implement a secure encryption system on susceptible devices. Standard encryption algorithms like RSA and AES are resource-intensive, require substantial memory, and can slow down devices. In this work, we introduce a lightweight cryptosystem that is suitable for low-powered devices without compromising security. This system utilizes Substitution-Permutation network, incorporating a variation of the Feistel design, to establish a secure symmetric key block cipher. As an enhancement over the standard technique, the proposed algorithm requires less run-time while still providing sufficient protection to maintain an avalanche effect of nearly 50%. © 2023 IEEE.

关键词： Block ciphers GFN IoT Knuth shuffle MATLAB S-box SPN

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Wildfire Spread Prediction using Machine Learning Algorithms 14

Wildfire Spread Prediction using Machine Learning Algorithms

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Aman Yadav, Rakesh Sudhamshu, Gadug Basnet, Aryan Ali, Rahmat University Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts. © 2023 IEEE.

关键词： Forecasting

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Fig Leaf Disease Prediction and Severity Estimation UsingDeep Learning

Fig Leaf Disease Prediction and Severity Estimation UsingDee...

引用

2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Prasanna Kumar, K.R. Gunasekar, M. Logeswaran, K. Dhanya Sree, P. Malathi, P. Vignesh, V. Dept. of Computer Science and Design Kongu Engineering College Tamil Nadu Erode India School of Computer Science and Engineering VIT Tamil Nadu Vellore India Dept. of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331510299

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1, 871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases. © 2024 IEEE.

关键词： AlexNet Deep Learning GoogLeNet Plant Disease prediction Severity Estimation VGG16

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Review on TIRADS Scoring Based Thyroid Nodule Malignancy Detection 9

Review on TIRADS Scoring Based Thyroid Nodule Malignancy Det...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and Data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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An Ensemble Model for detecting Sarcasm on Social Media 9

An Ensemble Model for detecting Sarcasm on Social Media

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Singh, Bhuvanesh Garg, Abhishek Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Nowadays, sarcasm is extensively utilized in regular conversations over social media sites. There have been cases where sarcastic statements have significantly reduced the accuracy in automatically detecting sentiment analysis or fake news. The paper presents natural language processing based text embedding techniques utilizing the ensemble approach in this study. The framework proposes the generation of text embeddings from Word2Vec, Global vectors for word representation (GloVe), and long short-term memory (LSTM) models. Word2Vec and GloVe are word embeddings based generators, while LSTM considers full text. Thus, the proposed framework encapsulates small tweets and sentences scenarios to detect sarcasm. Various natural language processing approaches have been developed, but each has textual context and proximity restrictions. Thus, the proposed framework overcomes this restriction by using word and sentence embeddings and ensembling the output. Two social media datasets, the Headlines dataset and the Twitter dataset, are used to test the proposed model. Headlines datasets have long sentences, while Twitter datasets have short tweets. The accuracy of 81.4% and 88.9% are achieved, respectively. The accuracy metric values are better than previous state-of-art frameworks. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Long short-term memory

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Tech-It-Easy: An Application for Physically Impaired People Using Deep Learning

Tech-It-Easy: An Application for Physically Impaired People ...

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2023 International Conference on Artificial Intelligence and applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Naveen, Devi Nirmala, M.D. Tejas Maladhkar, T. Serena, M. Mohis, Rahmath New Horizon College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456272

Physical limitations are and always will be a barrier to daily progress. Technology advancements are assisting everyone in leading simpler lives. Using the same technologies, we can provide a significant and beneficial answer to the issues associated with physical impairment. This essay discusses the use of technology to improve daily life for those who have physical or sensory disabilities. Not just for those who have hearing loss, but also as a tool for those who have speech disability, sign language is a vital means of communication. People without disabilities have a hard time understanding sign language, and specialists are frequently the only ones who can. Hence, a tool for sign language interpretation becomes necessary. Although Braille is a reading and writing system used by people who are blind. Braille is less popular among persons who are visually impaired, as it is time-consuming to manually translate every text into braille. Our study examines the issues raised by these two deficits and looks for technical remedies. Text to audio conversion is a piece of technology that can revolutionize the way visually impaired individuals communicate currently. It is simple and has been done effectively for the past ten years to convert written text to audio. In addition to sign language interpreters, a relatively new concept for assisting the education of the blind is to translate speech into sign language. The technologies stated above are anticipated to significantly improve the daily lives of people with physical disabilities, and this project can be further customized to match any suitable smart object. © 2023 IEEE.

关键词： Convolutional neural networks

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Cardiovascular Stroke Prediction System using Machine Learning Techniques 9

Cardiovascular Stroke Prediction System using Machine Learni...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Anandkumar, K.M. Mohanakrishnamoorthy, R. Salman Faris, F. Mohan Raj, B. Easwari Engineering College Autonomous Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9798350397376

Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. Early detection of heart conditions and clinical care can lower the death rate. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using machine learning techniques such logistic regression, SVM, Multinomial Nave Bayes, Random Forest, and Decision Tree. In most cases, input is received through numerical data of various parameters, and output findings are generated in real-time, predicting whether or not the patient has a disease. We'll use a variety of supervised machine learning methods before deciding which one is best for the model. Existing systems rely on classical deep learning models, which are inefficient and imprecise. They aren't as accurate as the proposed model and take a little longer to process. © 2023 IEEE.

关键词： Heart

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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Overview of Wireless Body Area Networks 7

Overview of Wireless Body Area Networks

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Kulkarni, Nitya N. Nagaraja, G.S. R v College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

Accessing everything remotely has become necessity as well as comfort of everyone in this technology advanced world. Monitoring health of a person remotely has also been the requirement with increase in ageing population. Wireless Body Area Networks (WBAN) have proved to be the most efficient solution to this problem. WBAN are short range communication network that include sensor nodes attached or implanted to the human body capturing the vital signs to further be used in decision making of the application. The applications of WBAN are widespread from remote health monitoring of patients in healthcare sector to assessing the fatigue of a soldier at the war front. Consequently, the procedures and protocols used by WBAN have to be efficient and reliable. However, there are several challenges faced in the successful real time implementation of WBAN as the devices used in it have limited energy and have to cope up with the movements and activities of a human body. With the intention to gain insights into these special networks, this manuscript presents a comprehensive overview of key principals, routing protocols and challenges of WBAN. Also, existing routing protocols are analysed with respect to energy consumption, packet delivery ratio, average delay, energy efficiency and average rise in temperature parameters. Analysis has led to the inference that there are very few routing protocols that can achieve efficient communication in WBAN. Thus, paving the path for further investigation to achieve a healthier world wherein people can proactively decide upon their health conditions. © 2023 IEEE.

关键词： Routing protocols

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Hazardous Asteroid Prediction Using Machine Learning 2

Hazardous Asteroid Prediction Using Machine Learning

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Babu Rao, M. Alekhya, G. Aakanksha, J. Naga Jyothi, K. SR Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India

ISBN: (纸本)9798350347982

With the help of machine learning algorithms, make a prediction of potentially hazardous asteroids (PHAs) which are having different combinations of orbital parameters. The suggested method intends to identify all major groupings of PHA-rich near-Earth asteroids (NEAs) and their subgroups. we applied different machine learning methods namely Random Forest, decision tree, k nearest neighbours, Naive bayes, Logistic Regression, Support Vector Machine (SVM) - for identifying potential hazardous and non-hazardous asteroids and features responsible for claiming an asteroid to be hazardous. so far as, asteroids are considered, there are many asteroids which are nearer to the earth, but all are not hazardous. So, our target in this project is to identify the asteroids which are hazardous and classify them with non-hazardous types. For this, we choose machine learning models. We used the data attributes to train these models, and then we compared the results to choose the model that provided the greatest accurate categorization. © 2023 IEEE.

关键词： Support vector machines

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