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An Improved Leukemia Detection Model using CNN

An Improved Leukemia Detection Model using CNN

引用

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

作者： Aditya, Y. Durga, M. Venkata Siva Naga Sai Teja, K. Hari Priya, J. Eswar Datta, K. Sr Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India

ISBN: (纸本)9798350397208

Leukemia is a kind of cancer in which a significant number of immature lymphocyte blood cells form, interfering with the formation of other blood cells. Normally, Leukemia can be identified by using Complete Blood Count(CBC) test. If there is too few or too many of one type of blood cells are presents in CBC then that indicates Leukemia. Even though leukaemia affects children 80% of the time, it can be fatal when it affects adults. So Early detection of Leukemia is very important to save lives. computer technology can help in quick Leukemia diagnosis. This study uses Convolution Neural Network (CNN) model for classifying microscopic blood cells images to predict whether the person is having Leukemia or not. © 2023 IEEE.

关键词： Diseases

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Vulnerability Scanning and Analysis Tool (VSAT) 9

Vulnerability Scanning and Analysis Tool (VSAT)

引用

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

作者： Nair, Aarya Anil, Abhinav Pallivathuckal, Anna Rose Daiju, Dijo John, Jomina Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Due to exponential technological advancement, the number of cyber attacks has been steadily increasing and leading to substantial loss of profits, productivity losses, reputational damage as well as judicial liability. To ensure that cyber attacks do not occur, it is important to identify vulnerabilities in web applications. This paper presents a vulnerability scanning tool that involves continuous monitoring and testing of Web applications which analyses the various vulnerabilities that are present in a system. Different types of scans that are performed to detect these vulnerabilities are also explained. © 2023 IEEE.

关键词： Network security

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Sign Language Identification using Skeletal Point-based Spatio-Temporal Recurrent Neural Network 9

Sign Language Identification using Skeletal Point-based Spat...

引用

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

作者： Johnson, Jans Joseph, Jisha Reji, Maris George, Megha Elisa Sajanraj, T.D. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Sign language is a visual language that serves as the primary means of communication in the non-hearing community. It is essential to research and develop sign language translation methods to enable smooth communication between the non-hearing and the hearing communities. The non-hearing community faces several challenges that hinder their interaction with the general public, such as the lack of knowledge of sign language and the availability of interpreters. A Sign Language Converter would thus be an important tool in breaking the communication barrier between the non-hearing and hearing communities. The paper aims to build a model that converts Indian Sign Language into corresponding words. We plan on using a skeletal-point feature extraction framework to identify hand landmarks from sequences containing distinct signs and use these landmarks to build a model for recognizing hand gestures using various Long Short-Term Memory (LSTM) Networks. [1] This approach can produce an accurate result compared to the traditional approach. The dataset is made by the co-authors of this paper due to the insufficiency of pre-existing ISL datasets. The user will be monitored and using the machine learning techniques discussed above, which will perform the real-time translation to display the final result. © 2023 IEEE.

关键词： Audition

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'Unlocking the Future of Stock Market Trend Prediction: Advancements with Long Short-Term Memory (LSTM) Models' 14

'Unlocking the Future of Stock Market Trend Prediction: Adva...

引用

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

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

ISBN: (纸本)9798350335095

The stock marketplace is a complex and dynamic environment where traders attempt to make informed choices to maximize their returns. Predicting the destiny tendencies of stock prices is a difficult project due to various factors influencing marketplace conduct. In recent years, deep mastering strategies, along with long short-term memory (LSTM), have won enormous interest for their capacity to capture elaborate styles and dependencies in time-collection information. This paper aims to develop an LSTM-based stock market trend prediction model with the objective of achieving significantly higher accuracy compared to existing approaches. The goal is to generate valuable insights from the predictions, enabling investors and traders to make more informed decisions in the stock market. These insights could include identifying potential trends, forecasting market movements, or suggesting optimal trading strategies. © 2023 IEEE.

关键词： Long short-term memory

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Comparison of CNN and YOLOv5 For Melanoma Detection 14

Comparison of CNN and YOLOv5 For Melanoma Detection

引用

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

作者： Antony, Divya Naseer, C. Government Engineering College Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9798350335095

MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score © 2023 IEEE.

关键词： Genetic programming

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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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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...

引用

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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Internet of Things and Artificial Intelligence Enabled Smart Classroom 6

Internet of Things and Artificial Intelligence Enabled Smart...

引用

6th International Conference on Electrical Information and Communication Technology, EICT 2023

作者： Akter, Sharmin Asaduzzaman, Asaduzzaman Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798350370836

Smart classrooms are now a buzzword in the field of education, both at home and abroad. With the advancement of technology, everything is getting automated and faster. Classrooms are not stepping behind either. In this work, a smart classroom is developed with several important features, like the total number of students in the classroom, a geolocation-based individual students' attendance system, and student attentiveness detection using eye blink *** prediction of the number of students is done using Machine Learning-based techniques based on environmental sensor data. The data collection module is designed using CO2, temperature, and humidity sensors. A dataset is prepared by collecting real-time data and building a multiclass prediction model based on sensor data to predict the number of students. The individual students' attendance system is developed using the existing WiFi system around the classroom, where students are connected to the WiFi network from their registered mobile devices. The geographical location of their devices is compared with the classroom location. A noble approach is also proposed to monitor the attentiveness of students by their eye blink rate, which is also monitored on the mobile device available to them. The results are shown to validate the effectiveness of the proposed systems. Finally, all the features are integrated into a graphical user interface. © 2023 IEEE.

关键词： Students

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Stock Price Prediction using Various LSTMs 2

Stock Price Prediction using Various LSTMs

引用

2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Narayana, Satyala Keerthi, Kokkiligadda Stuthi Srinivasulu, Anneboina Keerthika, Vampugani Prakash, Teki Veera Venkata Satya Sr Gudlavalleru Engineering College Dept. of Computer Science & Engineering Gudlavalleru India

ISBN: (纸本)9781665491990

In the world of the stock market, everyone wants to make money with fewer risks. Owing to the stock market's complexity, even if you do manage to understand it, you can never truly say that you do. For predicting share prices, a wide range of estimation techniques, which include technical analysis and fundamental analysis, have been employed. Due to its uncertainty, people still find it challenging to predict even after that. Additionally, new investors will continually enter the stock market at regular intervals. The fundamental issue faced with the current models, especially recurrent neural networks, is disappearing gradient. Consequently, the process of fully grasping the stock market and aiding investors in generating profits continues to insist on the use of varied deep learning methodologies to anticipate future stock prices. The five Deep Learning (DL) Long Short-Term Memory (LSTM) models used in this study, each demonstrated promising forecasting results for the same time series when one-step ahead predictability for stock market prices was performed. The findings are confirmed using RMSE computation. © 2023 IEEE.

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 ...

引用

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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