2023 2nd International Conference for Innovation in Technology (INOCON)最新文献

Construction of Computer Front-End Resource Sharing Platform Based on Web 基于Web的计算机前端资源共享平台的构建

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101273

Jihua He

引用次数: 1

Metal-Semiconductor – Metal structure on Graphene Doped ZnO Thin Film 石墨烯掺杂ZnO薄膜上的金属-半导体-金属结构

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101294

Harsha Sai Kalyanapu, Nandan Vemuri, Venkata Pavan Sai Hemanth Rayapati, A. Yadav, Gnana Prasanna Yella

引用次数: 0

Multiple Agents based Disaster Prediction for Public Environments using Data Mining Techniques 基于数据挖掘技术的公共环境多agent灾害预测

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101148

U. Malviya, S. Chauhan

{"title":"Multiple Agents based Disaster Prediction for Public Environments using Data Mining Techniques","authors":"U. Malviya, S. Chauhan","doi":"10.1109/INOCON57975.2023.10101148","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101148","url":null,"abstract":"Real-time data on natural disasters are collected, explained, analysed, predicted, and shown in the disaster management system. The development of GIS-based informational understanding has been documented (GIS). Using GIS and geographic data mining, the disaster management approach can pinpoint the epicentre of an occurrence and direct relief workers along the safest possible paths to the scene. The precise geological state and geographical placement of many areas makes them vulnerable to a wide range of natural disasters, including earthquakes, floods, land debris, landslides, cloud bursts, and human casualties. An efficient real-time system for predicting natural occurrences and locations is necessary to minimise damages and suffering. This research presents a unique methodology for predicting the location of disasters using density-based spatiotemporal clustering and global positioning system data. Before implementing clustering and feature selection, the process of data cleansing removes redundant, irrelevant, and inconsistent information from the news databases based on natural events. Areas prone to natural disasters like earthquakes, floods, landslides, and so on will be culled using a spatiotemporal clustering technique. The clustered data is then sorted by terms associated with natural catastrophes, and features are selected accordingly. In order to aid event detectors and location estimators, extracted features are supplied to a decision tree, which then categorises the data into both positive and negative classes.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194861","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

Disease Classification in Bell Pepper Plants Based on Deep Learning Network Architecture 基于深度学习网络架构的甜椒病害分类

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101269

Midhun P. Mathew, Sudheep Elayidom, Vp Jagathyraj

{"title":"Disease Classification in Bell Pepper Plants Based on Deep Learning Network Architecture","authors":"Midhun P. Mathew, Sudheep Elayidom, Vp Jagathyraj","doi":"10.1109/INOCON57975.2023.10101269","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101269","url":null,"abstract":"In modern days, artificial intelligence plays an important role in every scenario of life. Our economy mainly relies on agriculture, so this backwardness of technology affects the economy. When we are concerned about agriculture, the main issue that the agriculture sector facing now is, disease identification. Identification of diseases in the correct time can avoid loss of crops and finance of cultivator. Most farmers depend on a traditional method of detection, this method requires enormous amounts of work and time, but correctness of prediction is low. This Paper mainly focuses on disease identification in bell peppers in large farms based on deep learning networks such as Vgg 16, Vgg 19, and AlexNet. Generally, farmers won’t able to find out whether their plant is affected by diseases or not. The spread of diseases affects crop production. Only method to avoid the loss of crop production is by identifying the diseases at its early stage. We do testing based on the image from the different parts of the farm. We also intend to study pre-trained CNN architecture of VGG and AlexNet known as transfer learning, to detect disease detection in bell pepper. Based on our study we found out that Vgg 19 has better performance for disease detection in bell pepper.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124739789","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

Ensemble deep learning fusion for detection of colorization based image forgeries 基于彩色图像伪造的集成深度学习融合检测

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101337

Shashikala S, D. K.

{"title":"Ensemble deep learning fusion for detection of colorization based image forgeries","authors":"Shashikala S, D. K.","doi":"10.1109/INOCON57975.2023.10101337","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101337","url":null,"abstract":"Image forensics detects manipulation of digital images by tampering and counterfeiting process. While most works on Image forensics detect splicing, retouching and copy-move, very few have addressed colorization forgeries. Colorization or Fake colorization is a rapidly emerging area where colors of certain regions in image are manipulated with realistic colors. This is done maliciously to confound object recognition algorithms. Though some works are proposed to detect fake colorization, they can be deceived easily by introducing the pixel differences using statistical techniques. This work proposes a deep learning technique for detection of colorization forgeries which is resilient against deceiving attacks. Best set of discriminating features are extracted from Deep learning layers to recognize the differences in multiple channels of hue, saturation, value with aim to increase the accuracy of colorization forgery detection. Compared to most recent histogram based features, deep learning model is able to learn more intricate features about the distribution of intensity in hue, saturation, dark and value channels. Through experimental analysis, the proposed solution is found to provide at least 2% higher fake colorization detection accuracy compared to existing works","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126119839","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

Automated Script Evaluation using Machine Learning and Natural Language Processing 使用机器学习和自然语言处理的自动脚本评估

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101281

Sagarika M Chavan, M. S. Prerana, Ramit Bathula, Sreenath Saikumar, Geetha Dayalan

引用次数: 0

Control Strategy for Renewable Energy System Using Transformerless HERIC Bridge Inverter 无变压器HERIC桥式逆变器可再生能源系统控制策略

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101335

V. Saravanakrishnan., N. Dhanush, S. Israk Hussian., G. Thamizhselvan., A. Janagiraman

{"title":"Control Strategy for Renewable Energy System Using Transformerless HERIC Bridge Inverter","authors":"V. Saravanakrishnan., N. Dhanush, S. Israk Hussian., G. Thamizhselvan., A. Janagiraman","doi":"10.1109/INOCON57975.2023.10101335","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101335","url":null,"abstract":"This paper presents a transformer-less electrical converter is a power converter that synthesizes voltage from several levels of DC voltages. In Transformer-less electrical converter, selective harmonic elimination is major one for each medium-and high-voltage applications. The performance of output voltage and power is increased with the reduction of total harmonic distortion. Harmonic elimination in Transformer-less inverters established huge thought for past few decades with the smallest total harmonic distortion. But, the elimination of selective harmonics in Transformer-less electrical converter still experiences certain drawbacks. Recently, several analysis works are introduced for enhancing the performance of output voltage and power in transformerless electrical converter. In an existing hybrid formula was projected in seven-level Transformer-less electrical converter for selective harmonic elimination with condensed switches. HERIC optimization based mostly Selective harmonic elimination methodology is proposed for selective harmonic elimination with known optimum switching angle. Then, switching angle is measured and initialized for activity of the memetic optimization. The output voltage improvement is earned by proposing a shuffled frog leaping switching angle optimization based mostly selective harmonic elimination methodology in transformer-less inverters. After initializing switching angles, shuffled frog leaping switching angle optimization algorithm is utilized for selective harmonic elimination. A 120W model was also designed and tested in the laboratory, and the simulation and experimental results are finally conferred to show the wonderful performance of the proposed PV electrical converter.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125378485","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

Interpreting Machine and Deep Learning Models for PDF Malware Detection using XAI and SHAP Framework 使用XAI和SHAP框架解释PDF恶意软件检测的机器和深度学习模型

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101116

Tahsinur Rahman, Nusaiba Ahmed, Shama Monjur, Fasbeer Mohammad Haque, Muhammad Iqbal Hossain

引用次数: 0

Renewable Energy Systems Energy Modeling using Deep Learning Techniques 使用深度学习技术的可再生能源系统能量建模

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101286

Suryanarayan Sharma, D. Yadav

{"title":"Renewable Energy Systems Energy Modeling using Deep Learning Techniques","authors":"Suryanarayan Sharma, D. Yadav","doi":"10.1109/INOCON57975.2023.10101286","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101286","url":null,"abstract":"Communities using Sustainable Energy Systems (RES) seek to meet their electrical needs while reducing their reliance on public utilities by integrating renewable energy sources. Additionally, an intelligent micro grid makes it simple to access services for controlling energy use, which might lower utility costs for locals. These infrastructures are influenced by ML technologies, big data, AI, the IoT, and sensor technologies. New advancements in ML technology are required to produce precise learning approaches that can be used in the electricity analytical process, such as such monitoring, forecasting, prediction, scheduling, and decision-making. This will improve power control assistance and the spread of renewable energy sources. However, as the complexity of issues with the smart grid system, such as non-linearity and unpredictability, rises, so does the complexity of the resulting energy data format. The learning process cannot be completed by the fundamental ML approach since it can only evaluate fundamental raw data. Therefore, despite the data’s intricate and extensive structure, the Deep Learning (DL) approach may be used. A Convolutional Neural Network (CNN) will be developed in this study as a learning model to provide precise forecasts of future power usage and renewable energy installations. The echo state network is used to learn temporal features once interesting patterns have been retrieved from the past using the convolution process. The resultant spatiotemporal feature representation is ultimately given to fully connected layers for prediction. The proposed method was developed after thorough testing of both deep learning and machine learning models. When compared to state-of-the-art models, the results show that the recommended model performs as a model for energy equilibrium among production resources and consumers, with significant decreases in forecasting errors using MAE, MSE, RMSE, and NRMSE metrics.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122981688","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

CFD Simulation of Tier 4 Data Center for Cooling and Backup Power Tier 4数据中心冷却和备用电源的CFD仿真

2023 2nd International Conference for Innovation in Technology (INOCON) Pub Date : 2023-03-03 DOI: 10.1109/INOCON57975.2023.10101234

R. Balakrishnan, M. Munirajulu

引用次数: 0