2022 2nd Asian Conference on Innovation in Technology (ASIANCON)最新文献_第8页

Sentiment Classification of English and Hindi Music Lyrics Using Supervised Machine Learning Algorithms 使用监督机器学习算法的英语和印地语音乐歌词情感分类

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908688

S. N., Shruti Wagle, Priyanka Ghosh, Karishma Kishore

引用次数: 0

Remote-Controlled Multipurpose Road Cleaner 遥控多功能道路清洁器

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908934

M. Sivachitra, S. Dinesh, B. Gowtham, K. S. Vinothraja, S. Eraianbu

{"title":"Remote-Controlled Multipurpose Road Cleaner","authors":"M. Sivachitra, S. Dinesh, B. Gowtham, K. S. Vinothraja, S. Eraianbu","doi":"10.1109/ASIANCON55314.2022.9908934","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908934","url":null,"abstract":"An environment is a habitat where humans, plants, and animals all live together. Cleaning is an important part of the day-to-day routine. We have to maintain the place clean so that we can walk around the streets feeling fresh. A filthy environment leads to a deterioration of society, the emergence of diseases, and a slew of other issues. Humans are currently using pulling machines for cleaning which is usually done when there is no traffic on the roadways. But during bad conditions like pandemics, if the humans are directly involved in the cleaning process, there are high possibilities of getting diseases. The usage of remote-controlled cleaners will assist sanitation workers in preventing the transmission of diseases. The risk of getting affected by the diseases is reduced when the machine-controlled cleaner is remotely operated by the sanitizing workers. There are several cleaners available on the markets which can operate automatically, but they are mainly used to clean house floors. This paper aims to design and build a remote-controlled cleaner with sanitizer and cleaner that can safely clean roads and public places.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131730767","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

Second Generation Voltage Conveyer based Comparator and its application as Pulse Width Modulator 基于电压传送带的第二代比较器及其作为脉宽调制器的应用

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908730

Abhinav Anand, R. Pandey

引用次数: 0

Human Gesture Recognition Using CNN 使用CNN的人类手势识别

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909307

M. Rani, G. Andurkar

引用次数: 0

Face and Palm Identification by the Sum-Rule and Fuzzy Fusion 基于和规则和模糊融合的人脸和手掌识别

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909326

Kishore K. Singh, S. Barde

引用次数: 0

Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach 联邦学习在医疗保健领域的应用，使用软件定义的网络方法进行恶意软件检测和缓解

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909488

Dinelka Panagoda, Chathura Malinda, Chamod Wijetunga, L. Rupasinghe, Bathiya Bandara, C. Liyanapathirana

{"title":"Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach","authors":"Dinelka Panagoda, Chathura Malinda, Chamod Wijetunga, L. Rupasinghe, Bathiya Bandara, C. Liyanapathirana","doi":"10.1109/ASIANCON55314.2022.9909488","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909488","url":null,"abstract":"This research takes us forward with the concepts of Federated Learning and SDN to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital ICEs, the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929024","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

Transfer Learning: Convolutional Neural Network-AlexNet Achieving Face Recognition 迁移学习:卷积神经网络- alexnet实现人脸识别

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908650

Shatvik Singh, Sugandha Sharma, Amit Jain, Pritpal Singh, Animesh Kudake

{"title":"Transfer Learning: Convolutional Neural Network-AlexNet Achieving Face Recognition","authors":"Shatvik Singh, Sugandha Sharma, Amit Jain, Pritpal Singh, Animesh Kudake","doi":"10.1109/ASIANCON55314.2022.9908650","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908650","url":null,"abstract":"Nowadays, Machine-based face recognition is becoming very commonplace, robust and dependable system that is widely employed in numerous cases for access control. As in traditional approach, face recognition needs the extraction of face features prior to classification and recognition, which affects recognition rate. We employ Face Verification checks whether the pictures are associated with a single individual, whereas Face Identification must identify a specific face from a collection of known profiles in the system. To tackle this question, this paper incorporates the CNN structure Alexnet to obtain face identification.Throughout this article, we perform facial recognition using transfer learning in a Siamese network composed of 2 comparable CNNs. A pair of 2 face picture is fed into the Siamese network as input, after which the network learns the traits of this pair of pictures.Next the network is trained using the PRelu activation function to find the ideal learning algorithm and maximal values. Then, the face was identified and categorized. Library Multi-Spectral Face Data - set and Library 2D Faceprint Database were used to test the methodology, it enhances the accuracy of face recognition when compared to algorithms trained on datasets with a particular dataset and a specific spectrum’s recognition rate peaked up to 98 percent.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774475","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

Low Profile Edge-Stamp Vertical Polarized Antenna For Smart Metering IoT Applications 用于智能计量物联网应用的低轮廓边缘压印垂直极化天线

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908922

Hemin Ismael Azeez

引用次数: 0

Rapid Image Super Resolution 快速图像超分辨率

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908719

Keshav Gupta, Divyansh Goel, Divyani Divyani, Varun Sangwan

{"title":"Rapid Image Super Resolution","authors":"Keshav Gupta, Divyansh Goel, Divyani Divyani, Varun Sangwan","doi":"10.1109/ASIANCON55314.2022.9908719","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908719","url":null,"abstract":"Image Super-Resolution (ISR) is a long-established challenge that finds extensive usage in the field of medical imaging, media consumption, drone surveillance, etc. Recent advancements in deep learning and improved GPU hardware have enabled researchers to create sophisticated research work. Earlier approaches focused on improving the Peak-Signal-to-Noise-Ratio of SR images, but it led to the loss of finer details. Recently GAN-based architectures like SRGAN and ESRGAN have been introduced which improves the human-perceived quality of the generated SR images. However, these architectures have high computational costs, not suitable for low-end or mobile devices. We propose a lighter, faster, and optimized GAN-based super-resolution architecture, Rapid-SR, using depthwise convolutional layers. It produces similar results as state-of-the-art approaches while reducing the model parameters and the time taken to produce SR images substantially. We also use a novel training strategy for Rapid-SR which incorporates the measure of the perceived similarity in the training loss by using Learned perceptual image patch similarity (LPIPS). The results are analyzed and compared using PSNR/SSIM, LPIPS, and Mean Opinion Scoring.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129014392","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

Alzheimers Disease Recognition using CNN Model with EfficientNetV2 基于CNN模型的阿尔茨海默病识别

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908834

A. Raj, Sumit Bujare, Anil Gorthi, Jahan Malik, Abhradeep Das, Ashish Kumar

{"title":"Alzheimers Disease Recognition using CNN Model with EfficientNetV2","authors":"A. Raj, Sumit Bujare, Anil Gorthi, Jahan Malik, Abhradeep Das, Ashish Kumar","doi":"10.1109/ASIANCON55314.2022.9908834","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908834","url":null,"abstract":"In recent years, machine learning-based identification of Alzheimer's disease (AD) from the use of brain images such as MRI has been a hot topic. Deep learning's recent triumph in object recognition has stimulated related research. Such algorithms, however, have some disadvantages, including the requirement for a large number of training pictures and rigorous deep network design optimization. To address these issues, we employ CNN and ANN in this study. Weights are pre-trained on huge natural picture benchmark datasets in state-of-the-art designs like EfficientNetV2, and only a few numbers of MRI images are used to retrain the comprehensive layers in state-of-the-art architectures like EfficientNetV2. Our dataset contains 4 characteristics, as well as 5121 training shots and 1279 testing photos. The data was initially divided into three sets: training, validation, and test, with just the training, validation sets being used to select models. To minimize overfitting, the tests were abandoned undisturbed until the collaborative process was completed. The various techniques performed well when applied to when using datasets with alternative criteria for inclusion or demographic features, this is not the case.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116322223","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: 3