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Brain Tumor Identification using Convolutional Neural Network 5

Brain Tumor Identification using Convolutional Neural Networ...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Sanapala, Swapna Rashmi, M.R. Özer, Tolga Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bengaluru560035 India Afyon Kocatepe University Technology Faculty Electric Electronic Engineering Afyonkarahisar Turkey

ISBN: (纸本)9798331504403

there has been increasing concern about the rising rates of brain tumors. Brain tumors can be classified as primary and secondary tumors. Primary tumors are developed in the brain and the secondary tumors are the one which spread to the brain from other parts of the body. Approximately 120 distinct types of brain tumors have been identified. Magnetic Resonance Imaging (MRI) scans are considered the most effective diagnostic tool for detecting breaches in the blood-brain barrier. the advantages of using MRI scanning for brain analysis include clear visualization of soft tissue without any side effects from ionizing radiation. Manual diagnosis by radiologists and physicians is time consuming, costly, and prone to inaccuracies. For effective treatment, timely detection of brain tumors is must. Computer-aided diagnosis using machine learning algorithms, along with digital image processing is effective in brain tumor classification. Convolutional Neural Network (CNN) is proposed in this paper for identifying the brain tumor because in CNN more features are extracted. Brain tumor images are collected from the patients MRI. CNN classifier is tested on these clinical data. Initially there was overfitting problem which was addressed using k-fold cross validation. the highest accuracy of the model was found to be 94.89%, precision 0.9448 with 3-fold cross validation for 5 dense layers. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Smart Nursery System Based on sensor Technology 24

Design of Smart Nursery System Based on Sensor Technology

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5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Yang, Xiang Mo, Fuhong Ren, Dongyan School of Computer and Information Engineering Guangxi Vocational Normal University Guangxi530000 China

ISBN: (纸本)9798400718106

With the development of sensor technology, smart agriculture has attracted widespread attention as an important part of agricultural modernization.[1] In agricultural production, nursery plays a crucial role in crop cultivation and yield. However, the traditional seedling nursery method has problems such as cumbersome manual operation, inaccurate environmental monitoring, and waste of resources. therefore, in this paper we will firstly detail the design of the smart nursery system, including the key steps such as sensor selection and arrangement, data acquisition and processing, and the development of the cloud platform and mobile application. Secondly, we will verify the feasibility and effectiveness of the system through experiments and tests, demonstrating its potential in improving agricultural production efficiency and sustainable development. Finally, we will explore the limitations and improvement directions of the system and look forward to the future development of smart nursery technology in agriculture. the experimental results show that the smart nursery system has significant advantages in improving nursery effect and saving resources. In the future, the system algorithms and functions can be further optimised to expand the application scope and promote the development of smart agriculture. © 2024 ACM.

关键词： Production efficiency

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Energy Optimization in Wireless sensor networks Based on Firefly Optimization Technique and Hybrid Eagle with Firefly Optimization Technique 5th

Energy Optimization in Wireless Sensor Networks Based on Fir...

引用

5th international conference on Optical and Wireless Technologies (OWT)

作者： Sinha, Rupa Mehra, Rekha Govt Engn Coll Ajmer Dept ECE Ajmer Rajasthan India

ISBN: (纸本)9789811916458;9789811916441

Wireless sensor networks (WSNs) consist of a number of sensor nodes working together for gathering and retransmitting data or information. WSNs have become increasingly popular due to their wide range of applications. they are typically used for remote environment monitoring in areas where supplying electrical power is difficult. the devices are powered by batteries and by alternative energy sources. Localization is used in WSNs to find the current location of the sensor nodes. Installation of GPS on each sensor node is expensive, further, manually configuring location details on each sensor node is not possible in dense WSNs. To make the deployment of WSNs economical, localization techniques are used. With the help of localization techniques, sensor nodes identify their location based on the information provided by an anchor node or beacon node. As the battery energy is limited for the sensor nodes, the different optimization techniques are required for energy optimization and localization. In this proposed work, the firefly optimization technique and the Hybrid Eagle with firefly Optimization technique for energy optimization with RSSI (Received Signal Strength Indication) positioning method is applied to complete the cluster and cluster head selection to optimize energy and power consumption and to increase network life cycle in WSN power consumption. the performance of both algorithms and parameters is executed in the MAT-LAB simulation platform.

关键词： WSN Localization Optimization Firefly and Eagle RSSI sensor node

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Fake face detection based on deep learning and frequency domain processing 5

Fake face detection based on deep learning and frequency dom...

引用

5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Wang, Mengzhe Fan, Pengyu Yang, Tao Xiamen University School of Informatics Xiamen China Harbin Engineering University School of Mathematical Sciences Harbin China

ISBN: (纸本)9798350310603

With the rapid development of artificial intelligence, various advanced image generation and processing methods continue to emerge. In terms of fake face images, most are generated by methods based on generative adversarial networks (GANs). the increasing spread of fake face images may cause social problems such as misleading public opinion or damage to personal reputation. In this paper,we proposes a facial synthesis detection method based on deep learning and frequency domain processing. In this method, the Facenet network is used to extract facial features from images and enhance them during the extraction process. then, fast Fourier transform (FFT) is used to analyze the extracted facial features in the frequency domain, and ResNet50 network is used to extract frequency domain features from the obtained information. Finally, the extracted facial feature vectors and image frequency domain features are fused, and the image is classified based on the obtained fusion features. this method was experimented on a 16-class dataset and achieved good detection results, compared with the optimal facial feature detection model and frequency domain feature detection model. © 2023 IEEE.

关键词： Fast Fourier transforms

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Brain Functional Connectivity Classification Method for Voice Pitch processing in the Deaf Based on One-Dimensional Convolutional networks 5

Brain Functional Connectivity Classification Method for Voic...

引用

2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Shi, Mengrui Li, Qiang Engineering College for the Deaf Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350323313

this study aims to investigate the differences in brain functional connectivity between deaf children and hearing children in processing three different tones (first tone, second tone, third tone) using deep learning methods. Additionally, the study aims to identify the disparities in brain activation regions during tone processing between deaf and hearing children. Additionally, the goal is to identify the variations in activated brain regions during tone processing between deaf and typically developing children. Five deaf children and two typically developing children were selected as participants. Resting-state functional magnetic resonance imaging (fMRI) scans were conducted using a functional MRI scanner. Subsequently, data preprocessing and fMRI data analysis were performed. A one-dimensional convolutional network was employed to extract features and classify the input brain functional connectivity. through a series of theoretical and comparative analyses, it was determined that the one-dimensional convolutional network is capable of capturing local patterns within sequential data. It can extract information related to connectivity patterns from the one-dimensional sequence of connection weights, making it more suitable for processing brain functional connectivity data in deaf children. © 2023 IEEE.

关键词： Brain

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Message from the conference Chair

Proceedings - 2024 5th International Conference on Image Pro...

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Proceedings - 2024 5th international conference on Image processing and Capsule networks, ICIPCN 2024 2024年 xxvi页

作者： Pokharel, Manish Kathmandu University Nepal

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processing Delay Optimization for Data Intensive Applications for Fog/Cloud Computing Using Double Reinforcement Learning 5

Processing Delay Optimization for Data Intensive Application...

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5th IEEE international conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Nirupama, M. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science Karnataka Tumakuru India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305449

With smartphones becoming our daily companions, it has generated high-quality mobile services everywhere and there is tremendous growth of mobile traffics. this is going to becomea huge hindrance to the next generation mobile networks. Hence fog computing with cloud computing to deal with highly data-oriented applications sounds like a practical solution. Reinforcement learning is sub branch of machine learning which offers multiple solutions and the learning agent makes decision about superlative solution. A novel Double Reinforcement Learning based delay optimization framework is designed to optimize the processed data which need to be stored to fog/cloud storage system. the agent consider dual Q-states to formulate optimal policies for optimization of processing delay encountered by High Performance Computing (HPC) applications. the agent performs very well under varying uncertainty of large state space computing system like cloud/fog. the performance of the proposed framework is good pertaining to performance metrics like processing delay, learning rate, throughput, and cost of operation. © 2023 IEEE.

关键词： Reinforcement learning

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Route Optimizations Using Genetic Algorithms for Wireless Body Area networks 1

引用

5th international conference on Data and Information Sciences, ICDIS 2023

作者： Sharma, Gagan CSE Department Chandigarh University Mohali India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

there is a lot of research taking place in this fast and growing technology, namely Wireless Body Area networks (WBAN), to improve network performance. this work focuses to improve the WBAN system which is used in hospitals for the benefits of patients as well as doctors easiness. A lot of work can be identified in the same field and used as literature. Different researchers use different techniques to minimize energy consumption, packet delivery ration like Kruskal’s algorithm, Prim’s algorithm, particle swarm algorithm, etc. the energy of a sensor node gets impacted due to transmission distance, ideal routing protocols, and amount of data to be transmitted. Genetic algorithm (GA) which is a natural evolutionary algorithm and finds the optimal path to reach to destination which provides benefit to the patients in terms of their valuable data (patient’s report). Using GA along with fuzzy logic and cluster head selection, better results will be provided for packet delivery ratio and normalized residual energy, which further itself improves network lifetime. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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Data Reduction Techniques in Wireless sensor networks with AI 5

Data Reduction Techniques in Wireless Sensor Networks with A...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Arora, Gagan Deep Chopra, Navdeep Kumar Gopinath, N. Ravichandran, Sathish Kumar Chakravarthi, M. Kalyan Gangodkar, Durgaprasad Hyderabad India Chandigarh Engineering College Department of CSE Mohali Punjab Landran India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Christ University Department of Computer Science and Engineering Karnataka Bangalore India VIT-AP University School of Electronics Engineering Andhra Pradesh Amravati India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. the different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile. © 2022 IEEE.

关键词： Data integration

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Morphological Classification of Galaxies using Enhanced Deep Learning Model 5

Morphological Classification of Galaxies using Enhanced Deep...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. that is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. this attempt to build and train a CNN model using galaxy images as our domain. the Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. the Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. the Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. the Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. the Enhanced CNN outperforms the various existing state-of-the models. © 2024 IEEE.

关键词： Convolutional neural networks

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