2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)最新文献_第3页

COMPARATIVE ANALYSIS OF FOURTH GENERATION SOLAR CELL WITH COMBINATION OF ORGANIC AND INORGANIC MATERIALS 有机材料与无机材料结合的第四代太阳能电池的对比分析

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011004

Shahraan Hussain, B. Raj, S. S. Gill

引用次数: 0

Multi-Port Evaluation Framework for Android Software Design Android软件设计的多端口评估框架

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010759

Jidong Chen

引用次数: 0

Automatic Seed Generation for Weighted Test Pattern Generation 加权测试模式生成的自动种子生成

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010866

Undadi Shailaja, M. Samson

引用次数: 0

Application Programming Interface (API) based Student Activity Tracking System 基于API的学生活动跟踪系统

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010843

B. N, Monesh.M. S, P. C, V. S

引用次数: 0

Smart Design and Implementation of Self Adjusting Robot using Arduino 基于Arduino的自调节机器人智能设计与实现

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011083

R. G. Vidhya, K. Singh, P. Paul, T. A. Sai Srinivas, Jyoti Prasad Patra, K. Sagar

引用次数: 7

Automated Chest X-Ray Image Classification using Manta Ray Optimization with Deep Learning Approach 基于深度学习方法的蝠鲼射线优化自动胸部x射线图像分类

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010778

T. Kumar, R. Ponnusamy

{"title":"Automated Chest X-Ray Image Classification using Manta Ray Optimization with Deep Learning Approach","authors":"T. Kumar, R. Ponnusamy","doi":"10.1109/ICAISS55157.2022.10010778","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010778","url":null,"abstract":"Medical image classification has played a key role in teaching tasks and clinical treatment. But the conventional technique has reached its maximum performance. Furthermore, more effort and time should be spent on selecting and extracting classification features by using them. The deep neural network is an emergent machine learning (ML) technique that has shown its potential for numerous classification tasks. Especially, the convolution neural network predominates with better outcomes on different image classification models. This article develops an Automated Chest X-Ray Image Classification using Manta Ray Optimization with Deep Learning Approach (MRFO-DLA). The presented MRFO-DLA technique majorly concentrates on the recognition and classification of diseases using CXRs. To attain this, the presented MRFO-DLA technique designs a bilateral filtering (BF) approach is applied for image preprocessing stage. Next, the MRFO-DLA model employs neural architectural search network (NASNet) for feature extraction. At the final stage, the MRFO with autoencoder (AE) model is exploited for CXR classification. To demonstrate the enhanced performance of the MRFO-DLA method, a series of simulations were performed and the outcomes were inspected in diverse aspects. The simulation outcomes ensured the enhancements of the MRFO-DLA approach compared to recent techniques interms of different measures.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128939096","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

Prediction of Skin Cancer using Convolutional Neural Network 使用卷积神经网络预测皮肤癌

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010936

Karthikayani. K, Sadhana S, L. C, Suman A. Patil, Anandbabu Gopatoti, Shanta Phani

{"title":"Prediction of Skin Cancer using Convolutional Neural Network","authors":"Karthikayani. K, Sadhana S, L. C, Suman A. Patil, Anandbabu Gopatoti, Shanta Phani","doi":"10.1109/ICAISS55157.2022.10010936","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010936","url":null,"abstract":"Skin damage is one of the most fatal illnesses. Unassisted dissection and management at the beginning will result in it contacting other areas of the body. Due to the rapid progression of skin cells, it occurs when exposed to sunlight An automated system for skin sore recognition is expected to reduce effort, time, and human life for early detection. A technique for preventing skin cancer is based on both images and significant learning. An enhanced system is proposed in the paper for representing skin infections. In this study, nine types of skin illnesses were collected. It contains nine clinically significant skin cancers, such as actinic keratosis, basal cell carcinoma, innocuous keratosis, dermatofibromas, melanomas, nevus, seborrheic keratosis, and squamous cell carcinoma. Convolutional Neural Networks are used to classify skin diseases into various classes as well as diagnose the severity of them. In the diagnosis system, pictures are taken into consideration as well as significant learning is utilized. More pictures have also been added by using different methodologies. Finally, the deep learning approach also helps to ensure the task's precision. By using the proposed CNN procedure, an average accuracy of 96.12 percent is obtained.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130878652","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: 2

An Adaptive Load Balancing Technique for Multi SDN Controllers 多SDN控制器的自适应负载均衡技术

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010881

S. Praveen, Pappula Sarala, T. N. S. K. M. Kumar, Siva Ganesh Manuri, V. S. Srinivas, D. Swapna

引用次数: 7

Improving the Quality of Power by Compensating Voltage Sag Compensation using Dynamic Voltage Restorer 利用动态电压恢复器补偿电压暂降，提高电能质量

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011071

S. P., S. R, M. M, L. P, D. R, V. D.

{"title":"Improving the Quality of Power by Compensating Voltage Sag Compensation using Dynamic Voltage Restorer","authors":"S. P., S. R, M. M, L. P, D. R, V. D.","doi":"10.1109/ICAISS55157.2022.10011071","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011071","url":null,"abstract":"Consumers and utility companies alike may be negatively impacted by poor power quality. The integration of renewable energy sources, smart grid systems, and extensive use of power electronics equipment has resulted in a slew of problems in modern electric power systems. It is possible that harmonics in current and voltage as well as voltage drop, and swell may cause harm to sensitive electronic equipment. Interference from other sections of the system may cause input voltage changes in these devices. For this reason, in today's technologically advanced world, the reliability and safety of the power system depend on power quality. DVR, or Dynamic Voltage Restorer, is a D-FACTS (Distribution Flexible AC Transmission System) device that might be extensively used to address the challenges of the distribution grid's non-standard voltage, current, and frequency. By using Proportional Integral digital control signals as DVR, a new approach for reducing torque ripple has been created in this article. After that, control signals are supplied to the voltage source converter input of the brushless direct current motor's voltage source converter. This control approach reduces torque ripples. This aim is continuously met, and therefore the suggested model outperforms a few established techniques, such as FCS-MPC, PWM-MPC, and RSC, with the proposed Pulsating Proportional Integral Control (PPIC) based DVR. 3.2Nm average torque, 10.64 % torque ripple, 8.56I of RMS DC link current, 961.52 W power, 2333.9 rpm speed are achieved using the suggested approach.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115670285","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

Secure Image Steganography Technique based on Hybrid Transforms and Enhanced AES 基于混合变换和增强AES的安全图像隐写技术

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010850

Syed Basha Shaik, D. S. Devi, M. Sujithra, Sarika G, Vikas Tripathi, Dhiraj Kapila

引用次数: 0