2023 8th International Conference on Communication and Electronics Systems (ICCES)最新文献

Automatic Patient Monitoring and Alerting System based on IoT 基于物联网的患者自动监测报警系统

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192644

P. Anirudh, G. Kumar, R. Vidyadhar, G. Pranav, Bathula Anil Aumar

{"title":"Automatic Patient Monitoring and Alerting System based on IoT","authors":"P. Anirudh, G. Kumar, R. Vidyadhar, G. Pranav, Bathula Anil Aumar","doi":"10.1109/ICCES57224.2023.10192644","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192644","url":null,"abstract":"The Internet of Things allows for the creation of systems that use sensors, interconnect devices, and the Internet to achieve a higher level of automation. In healthcare, IoT has the potential to revolutionize patient monitoring and management. In an intensive care unit (ICU), silent monitoring is crucial as even a slight delay in making a decision regarding a patient treatment can lead to permanent disability or death. Devices placed on the body or integrated into living spaces are capable of gathering detailed information on an individual's physical and mental state.IoT in healthcare has a lot of potential and difficulties. To enhance clinical management, a new intelligent system for patient monitoring can be created. A novel blended design called as Autonomous Identification of Danger Situations and Alerts has been proposed for a visual health monitoring system. In this system, many cameras and cooperative clinical sensors are used, all of which are controlled by a single platform. However, there could be a few challenges in closely monitoring the health metrics captured by ICU equipment, which has a wide range of sensors.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117207312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Speech Emotion Recognition with High Accuracy and Large Datasets using Convolutional Neural Networks 基于卷积神经网络的高精度大数据集语音情感识别

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192891

S. G, Tamilvizhi T, T. V, S. R

引用次数: 0

Quantum Money : Opportunities, Challenges and Open Issues 量子货币:机遇、挑战和开放问题

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192676

Shashank Mishra, Akshat Ojha, Archit Aggarwal, Pawan Singh Mehra

引用次数: 0

LiDAR Sensor for Self-Driving Cars 用于自动驾驶汽车的激光雷达传感器

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192716

M. Tummala, P. A.

引用次数: 0

A Multi Stage Approach for Object and Face Detection using CNN 一种基于CNN的多阶段目标和人脸检测方法

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192823

Shaik Mohammed Zahid, T. Nashiya Najesh, Salman. K, Shaik Ruhul Ameen, Anooja Ali

{"title":"A Multi Stage Approach for Object and Face Detection using CNN","authors":"Shaik Mohammed Zahid, T. Nashiya Najesh, Salman. K, Shaik Ruhul Ameen, Anooja Ali","doi":"10.1109/ICCES57224.2023.10192823","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192823","url":null,"abstract":"Object and face detection are important tasks in computer vision that have numerous applications, such as surveillance, image recognition, and autonomous driving. Artificial intelligence (AI) has transformed the field of image recognition, enabling machines to interpret and analyze visual data with remarkable accuracy and speed. AI algorithms use deep learning techniques to automatically recognize patterns, shapes, and features within images, allowing them to identify objects, people, and even emotions. Image recognition has numerous practical applications, from facial recognition in security systems to medical imaging for diagnosis. The approach for object detection, face recognition, and celebrity identification proposed in this research uses algorithms such the Convolution Neural Network (CNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbor (KNN). The CNN model is proven to be more accurate than other models due to their ability to learn features from images. The multi-stage approaches for object and face detection using CNNs have shown to be effective in achieving high accuracy of 93.2% and real-time performance","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Packaging Virtual Image Auxiliary Generation Algorithm based on Large Language Model (LLM) 基于大语言模型(LLM)的包装虚拟图像辅助生成算法

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192861

Yang Zhou, Fan Zhang

引用次数: 0

Content Restricting Age Predictor System using Artificial Intelligence 人工智能内容限制年龄预测系统

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192683

R. P, V. T, Anushruthi N T, Goutham S, Gurusaran J

{"title":"Content Restricting Age Predictor System using Artificial Intelligence","authors":"R. P, V. T, Anushruthi N T, Goutham S, Gurusaran J","doi":"10.1109/ICCES57224.2023.10192683","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192683","url":null,"abstract":"Online safety, particularly for children, is a significant concern in today’s digital age. The proposed project aims to develop a combined framework to prevent children from accessing the age restricted content on the internet. The system involves utilizing a computer vision technique called Blob from image to detect face in the image. To achieve accurate age detection, the proposed approach employs a deep learning method called a convolutional neural network (CNN). CNN is particularly good at processing and recognizing images and has multiple layers, including convolutional, pooling, and fully linked layers, which extract the binary values and compare it with available data to provide the user’s age. The proposed approach uses the open-source automation tool, Selenium, to control access to websites based on the predicted age of the user. By restricting access to inappropriate content based on age, this approach provides a reliable way to safeguard children’s online experience. The combined framework of face recognition and age detection, in conjunction with the OpenCV and the Selenium tool, offers a reliable and effective approach to age detection and access control on the internet.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121837336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Brain Tumor using VGG16 应用VGG16检测脑肿瘤

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192639

Sasupalli Rohith, Marikanti Sai Prakash, R. Anitha, Korada Sasi Kumar, Kotta Yogeswara Sai

{"title":"Detection of Brain Tumor using VGG16","authors":"Sasupalli Rohith, Marikanti Sai Prakash, R. Anitha, Korada Sasi Kumar, Kotta Yogeswara Sai","doi":"10.1109/ICCES57224.2023.10192639","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192639","url":null,"abstract":"The detection of brain tumors plays a crucial role in medical imaging, and machine learning techniques have shown great potential in improving the accuracy and efficiency of this process. In recent years, deep convolutional neural networks (CNNs) such as VGG-16 have been successfully applied to this task, achieving high levels of accuracy in tumor detection. The VGG-16 model is a deep CNN architecture that has been trained on a large dataset of images, allowing it to learn complex features that are useful for classifying brain tumor images. By leveraging the power of transfer learning, the model can be fine-tuned on a smaller dataset of brain tumor images, allowing it to learn specific features that are relevant to this task. Here, we offer a method for leveraging the VGG-16 model to find brain cancers. We first pre- process the images to enhance the contrast and remove noise, then extract features from the images using the VGG-16 model. After that, these features are applied to build a SVM classifier to distinguish between images with and without tumors. The proposed results show that the VGG-16 model is highly effective in detecting brain tumors, achieving an accuracy of over 95%. This approach has the potential to significantly improve the efficiency and accuracy of brain tumor detection, allowing doctors to diagnose and treat patients more quickly and effectively.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing 基于卷积神经网络和边缘计算的人脸识别深度统一模型

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192630

V. Senthilkumar, P. Saranya, B. K. Rani, S. P, Ramu Kuchipudi, Md. Abul Ala Walid

{"title":"Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing","authors":"V. Senthilkumar, P. Saranya, B. K. Rani, S. P, Ramu Kuchipudi, Md. Abul Ala Walid","doi":"10.1109/ICCES57224.2023.10192630","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192630","url":null,"abstract":"CCTV, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. The variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). This paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). The suggested system had 99.1% accuracy on test data.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT and Hybrid Cloud for Smart Hospital Management 面向智慧医院管理的物联网和混合云

2023 8th International Conference on Communication and Electronics Systems (ICCES) Pub Date : 2023-06-01 DOI: 10.1109/ICCES57224.2023.10192855

Tarun Gadiraju, Durga Devi K, Akshay Raavi, Uday Kiran Pinapothini

{"title":"IoT and Hybrid Cloud for Smart Hospital Management","authors":"Tarun Gadiraju, Durga Devi K, Akshay Raavi, Uday Kiran Pinapothini","doi":"10.1109/ICCES57224.2023.10192855","DOIUrl":"https://doi.org/10.1109/ICCES57224.2023.10192855","url":null,"abstract":"In this research, Hybrid Cloud, the Internet of Things (IoT), and Artificial Intelligence (AI) are proposed to address the problems associated with the current hospital system. These problems include instances where irregularities may go undiscovered and result in patient health problems due to factors such as staff carelessness, high patient volumes, or neglectful family members. Hence, a system combining sensor technologies and the Internet of Things (IoT) has been recommended in this study. A study illustrates that the implementation of smart hospitals can successfully address the major issues that hospital diagnosis and treatment currently face, and it has a positive and significant impact on how hospitals currently approach diagnosis and treatment. Using this method, one can monitor a patient's condition, the saline bottle's level, their heartbeat, blood pressure, and temperature from a distance. The system makes use of IoT technology to link various medical devices and sensors to the cloud, hybrid cloud technology to store and manage vast volumes of data, and AI technology to evaluate this data and make real-time decisions based on the insights gained.","PeriodicalId":442189,"journal":{"name":"2023 8th International Conference on Communication and Electronics Systems (ICCES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129716663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0