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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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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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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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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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Overview of Wireless Body Area Networks 7

Overview of Wireless Body Area Networks

引用

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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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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Stegomalware: A Comprehensive Survey on Creation and Detection of Malware in Images 2

Stegomalware: A Comprehensive Survey on Creation and Detecti...

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2nd International Conference on Artificial Intelligence Trends and Pattern Recognition, ICAITPR 2024

作者： Mohan, Mili Sabitha, S. College of Engineering Trivandrum Affiliated to Apj Abdul Kalam Technological University Dept. of Computer Science & Engineering Kerala India Govt. Engineering College Palakkad Affiliated to Apj Abdul Kalam Technological University Dept. of Computer Science & Engineering Kerala India

ISBN: (数字)9798350368000

ISBN: (纸本)9798350368000

Cyberattacks are becoming more sophisticated as technology progresses. Since fraudsters have mastered the art of hiding dangerous code in digital media, photos are becoming a more and more common choice. Stegomalware is a type of cyberattack that combines steganography principles with malevolent intent, enabling attackers to conceal dangerous code within seemingly benign images. The primary purpose of these attacks is to bypass traditional defenses and exploit people's trust in seemingly benign image files. Attackers can evade detection systems such as intrusion detection systems, network firewalls, and antivirus software by hiding harmful payloads inside images. We provide a thorough explanation of the development process and historical occurrences of stegomalware in this work. Machine learning models may be trained to recognize patterns and traits typical of stegomalware in images by utilizing artificial intelligence. These algorithms are capable of reliably identifying concealed harmful payloads by analyzing massive datasets of benign and malicious photos. In this work, we examined a few machine learning-based methods that aid in the identification of stegomalware. Cybersecurity professionals may create more resilient defenses and proactive plans to combat future assaults by knowing the traits of stegomalware. © 2024 IEEE.

关键词： computer viruses

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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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Transcribing Natural Language to Python Code: A Compiler Based Approach 15

Transcribing Natural Language to Python Code: A Compiler Bas...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Coding in a programming language can indeed be a meticulous and a less interesting task, especially when compared to the fluidity of spoken or written communication. Each programming language has its own set of syntax and rules, demanding expertise to effectively write code in it. Through this study, a compiler based approach to translate Natural Language into Python code is proposed. This endeavor could revolutionize access to programming by making it more intuitive and inclusive. The research involves working with different phases of the compiler including the lexical analyser, syntax and the semantic analyser phase in combination with Natural Language Processing techniques. By automating this process, we aim to streamline programming for a wider audience, removing barriers to entry and making coding more accessible to all. © 2024 IEEE.

关键词： Code Generation Compiler Architecture Compiler-Based Translation Natural Language Processing (NLP) Semantic Understanding

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