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Advances in Deep Learning for Dermatology: Convolutional Neural Networks for Automated Skin Disease Detection and Diagnosis 14

Advances in Deep Learning for Dermatology: Convolutional Neu...

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

作者： Vasishtamol, P.K. Soni, P. Government Engineering College Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9798350335095

Skin conditions provide serious health risks and need a precise and prompt diagnosis in order to be effectively treated. Convolutional neural networks (CNNs), in particular, have demonstrated encouraging results in automated skin disease identification in recent years. In this paper, we describe a unique method for identifying seven distinct categories of skin illnesses using the HAM10000 image dataset, with a CNN-based model, and a 95 percent accuracy *** HAM10000 dataset, which comprises of dermatoscopic images, is preprocessed into seven classes: Benign keratosis,actinic keratoses,dermatofibroma , melanoma ,vascular lesions, melanocytic nevi and basal cell carcinoma. It reach a 95 percent overall accuracy on the testing set after much experimentation and *** results demonstrate that utilising the HAM10000 image dataset, the proposed CNN-based technique can successfully diagnose a wide range of skin diseases. The accurate and automated diagnosis of skin problems using deep learning techniques can greatly aid dermatologists and other healthcare professionals in offering quick and effective treatments. This study makes a significant contribution to the expanding area of dermatological computer-aided diagnosis and provides insightful information for future developments in skin disease detection. © 2023 IEEE.

关键词： Dermatology

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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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Comparative Analysis of VGG19 and ResNet50 V2 for Enhanced Brain Tumor Detection 3

Comparative Analysis of VGG19 and ResNet50 V2 for Enhanced B...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Fulara, Saurabh Upadhyay, Deepak Sharma, Kunj Bihari Rawat, Ramesh Singh Graphic Era Hill University Department of PDP Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372120

This paper compares the performance of two most common deep learning models ResNet50 V2 and VGG19 when using medical imaging for brain tumour detection. Even though early and accurate diagnosis is crucial to improving the treatment and survival of patients, traditional diagnostic methods are ineffective. The purposes of this paper are, first, to evaluate the performance and capabilities of VGG19 and ResNet50 V2 with regard to current achievements in convolutional neural networks using those for brain tumour detection, and second, to compare the accuracy of the results of both models. Based on the accuracy, precision, recall, and F1-scores of both models, the study obtained a general accuracy of 60%. The obtained results demonstrate marked trade-offs between the accuracy and recall of the two models, showing their major advantages and drawbacks. The conclusion of the paper deals with the possible implications of the study for practice and further research aimed to increase the efficiency and applicability of AI in medical diagnostics. Such studies may explore hybrid models, expansion of data augmentation, and the role of interpretability in the support for medical experts. © 2024 IEEE.

关键词： computerized tomography

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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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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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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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A Study on Various Approaches Towards Non-Factoid Question Answering Systems 2

A Study on Various Approaches Towards Non-Factoid Question A...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Lakshmi, K. Sindhu, S. Nss College of Engineering Dept. of Computer Science and Engineering Kerala Palakkad India

ISBN: (纸本)9781665456302

Question Answering (QA) is a branch of Natural Language Processing (NLP) which develop systems that automatically provide answer to questions queried by humans in any natural language. Depending upon the type of question, QA systems can be both factoid and non-factoid type. There are numerous methods for retrieving answers for the input queries. Due to its complex behavior, non-factoid QA still remains a challenge to many researchers. This survey mainly focuses on works done so far in the area of non-factoid QA systems. Beginning with traditional deep learning approaches and moving towards the usage of Knowledge-Graphs (KG), this paper focuses on studying how to improve the efficiency of such QA systems. Transformer models like Bidirectional Encoder Representations from Transformers (BERT) and its variants reflect a significant improvement in QA implementation. One of the main challenges faced by those models was to answer the questions from knowledge-dependable domains. The study shows that knowledge-graphs help to address these issues to some extent. Also, it provides an analytical discussion on various approaches in non-factoid QA system implementation and how KG outperform previous methods. © 2023 IEEE.

关键词： Knowledge graph

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IoT Based Smart Parking System 5

IoT Based Smart Parking System

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Izudheen, Sminu Gokul, P.V. Joseph, Jesbin Nair, Karthik S Manu Krishnan, N.S. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350334128

The number of vehicles being used for daily commute within the cities have increased exponentially since 2015. This has caused a major concern regarding parking spaces especially in a rapidly developing cities. More than 25% of vehicles driving around the cities are looking for a parking space. Numerous issues arise as a result of poor parking infrastructure such as significant wastage of time for drivers, traffic congestion and increase in car emissions. Implementing smart technology to facilitate this task will solve this problem, improving operational efficiency, simplifying the flow of urban traffic and offering drivers a more enjoyable and time-saving experience. The proposed IoT-based smart parking system allows real-time data about parking availability to be obtained via a mobile application. An IoT gadget, which includes sensors and microcontrollers is placed in each parking space. The data from the gadget will be pushed to a cloud-based system which analyses this data to calculate the availability of free slots in parking spaces. The users receive real-time information on the availability of all parking spaces giving them the choice to choose the best one. © 2023 IEEE.

关键词： Traffic congestion

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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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An Android App Scanner for Detecting the Freshness and Age of Poultry Eggs using Image Processing

An Android App Scanner for Detecting the Freshness and Age o...

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2023 IEEE International Conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Aniyan, Nisha Jacob, Pramod Mathew Pranav, P. Raj, Reshmi Ghosh, Gopika Suresh, Amitha Providence College of Engineering Dept. of Computer Science and Engineering Kerala Chengannur India

ISBN: (纸本)9798350397208

Grading food items based on their quality and freshness is essential for a healthy life. Quality and freshness are two incredible factors that people consider while buying food items. Quality prediction of food items like eggs is not possible by examining their appearance. Large cracks are the only defects that can be found through this observation. Traditional egg quality and freshness detection methods are destructive, time-consuming, inaccurate, and require constant monitoring. This paper proposes a model with an Android app scanner for detecting the freshness and age of poultry eggs using image processing techniques. This model can be used for checking the freshness and age of eggs by classifying them with various grades. The results of this model will be available in the Android app connected to this device. This model can be used for commercial as well as domestic usage. This model is non-destructive, time-saving, easy to use, flexible, and accurate. © 2023 IEEE.

关键词： Grading

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