2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)最新文献_第7页

Prediction of Photovoltaic Power Generation using Machine Learning - A Review 利用机器学习预测光伏发电-综述

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141769

Rachna, Ashutosh Kumar Singh

{"title":"Prediction of Photovoltaic Power Generation using Machine Learning - A Review","authors":"Rachna, Ashutosh Kumar Singh","doi":"10.1109/InCACCT57535.2023.10141769","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141769","url":null,"abstract":"The world environment crisis; leading towards zero carbon emission on one hand and the increase in electrical energy demand on the other hand has accelerated us to make better use of the non-conventional energy sources present on earth. Sun being the major source of non-conventional energy produces clean energy however, the weather conditions, day-night patterns, and seasons affect renewable energy production a lot. Machine learning can be a powerful tool to rescue us from this uncertainty in the case of renewable power production. This paper is a comprehensive review of various machine-learning techniques for predicting solar power generation by keeping track of solar irradiance, temperature, and other parameters that affect solar power generation. The paper provides insight into the ML techniques used previously, their benefits, and the best-suited multi-level ML techniques for the prediction of solar power generation. Further, we wind up our work by concluding the future scope of the discussion and proposing the ML methodologies that can be employed in the future in the field of power generation prediction using machine learning.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131242726","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

A Hybrid Deep Neural approach for multi-class Classification of novel Corona Virus (COVID-19) using X-ray images 基于x射线图像的新型冠状病毒(COVID-19)多类分类的混合深度神经方法

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141782

Abhishek Agnihotri, Narendra Kohli

{"title":"A Hybrid Deep Neural approach for multi-class Classification of novel Corona Virus (COVID-19) using X-ray images","authors":"Abhishek Agnihotri, Narendra Kohli","doi":"10.1109/InCACCT57535.2023.10141782","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141782","url":null,"abstract":"People all around the world are facing challenges to survive due to Corona Virus (Covid-19). Pneumonia is often caused by COVID-19. Biomedical field has witnessed the success of Artificial Intelligence (AI) models for automatic diseases analyses and detection. Deep Learning (DL), a sub-field of AI, is used in this work to classify COVID-19 from Normal and Pneumonia patients. Three architectures i.e., Novel Convolutional Neural Network (N-CNN), Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Random Forest (CNN-RF) models are proposed in this work for the classification of covid19 images from pneumonia and normal cases. We have used the X-ray image dataset in which 1212 training images consists of 404 images for each class and 300 validation images in which 100 images for each class. Five pre-trained models (VGG-19, VGG16, ResNet50, Inception v3 and Inceptio$mathrm{n}_{-}$ResNetv2) are used to compare the classification performance with the proposed models. Among these pre-trained models and three proposed models, CNN-RF model outperformed and achieved an accuracy of 94.66% whereas N-CNN and CNN-LSTM models got an accuracy of 89.67% and 90.33% respectively.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128680800","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

A Literature Review on Citrus Fruits and Leaves diseases detection using Deep Neural Network model 基于深度神经网络模型的柑橘果叶病害检测研究综述

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141763

C. Vinothini, Aditi Anand Huralikoppi, Kunche Nithyasree Royal, Guduru Rama Koushika, Koppala Jyoshna

引用次数: 0

Decentralizing File Sharing: The Potential of Blockchain and IPFS 分散文件共享:区块链和IPFS的潜力

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141817

Jyotsna Anthal, Shakir Choudhary, Ravikumar Shettiyar

{"title":"Decentralizing File Sharing: The Potential of Blockchain and IPFS","authors":"Jyotsna Anthal, Shakir Choudhary, Ravikumar Shettiyar","doi":"10.1109/InCACCT57535.2023.10141817","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141817","url":null,"abstract":"This research paper delves into the potential of using blockchain technology and the Interplanetary File System (IPFS) for file sharing. The current state of file sharing is analyzed, and it is found that centralized servers pose many challenges, such as security and privacy issues. The paper then investigates how blockchain and IPFS can provide a decentralized and secure solution for file sharing. The analysis of specific use cases and existing projects such as File Coin, which utilizes IPFS and a blockchain-based marketplace to allow users to rent out unused storage space, is also presented. The paper also addresses the limitations and challenges of using this technology, including scalability and regulatory issues. The research concludes that the combination of blockchain and IPFS has the potential to revolutionize the way digital content is shared and distributed, providing new opportunities for secure and efficient file sharing. The paper also explores other decentralized storage solutions, such as Sia, Storj, and MaidSafe, and how they compare to IPFS and blockchain-based file-sharing solutions. The research also evaluates the potential impact of this technology on various sectors, such as the media, entertainment, and healthcare industries. Overall, this research aims to provide a comprehensive understanding of the topic and its potential impact on the future of file sharing. The advancement of technology has impacted the way information is shared and distributed, with traditional file-sharing methods facing numerous security and privacy challenges. This research paper investigates the potential of using blockchain technology and the Interplanetary File System (IPFS) for file sharing as a means of addressing these challenges. This research paper argues that the combination of blockchain technology and IPFS holds great promise for the future of file sharing.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132391275","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

A Comparative Analysis on Influence Maximization Models in Social Networks 社会网络中影响最大化模型的比较分析

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141772

Agash Uthayasuriyan, G. Hema Chandran, Uv Kavvin, Sabbineni Hema Mahitha, G. Jeyakumar

引用次数: 0

An Integrating Computational Approach Review to Analyse the Biological Functions 生物功能分析的综合计算方法综述

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141836

S. Kimothi, Pooja Joshi, Sunil Shukla, Rajiv Kumar, Ishteyaaq Ahmad, M. Memoria

{"title":"An Integrating Computational Approach Review to Analyse the Biological Functions","authors":"S. Kimothi, Pooja Joshi, Sunil Shukla, Rajiv Kumar, Ishteyaaq Ahmad, M. Memoria","doi":"10.1109/InCACCT57535.2023.10141836","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141836","url":null,"abstract":"In present study the fractal theory has been reviewed in the context of bio-functional and biomedical complex systems. The chaotic approach is a critical component of the theoretical framework and can be used in analyzing complex biological structures such as chromatin structures. Fractality is a metric of complexity in biological functions; it is an indicator of the complication level of the self-similar structure, while chaos is a sort of dynamic behavior that usually produces totally arbitrary patterns. Fractal measurements in vivo could be used to predict the efficiency of painful therapy. The fractal technique can be used to assess carcinogenesis, tumor progression, chemoprophylaxis, and treatment with the convergence of modern sensing techniques in nano-scale spectroscopic techniques, which is a prospective biomarker. The mathematical principles of fractals and chaos in biological systems are presented in the context of the condition of health treatment and their significance. Fractality in different biological functions including the heart has now been investigated and measured the dosing quantity with chaos and fractal level. As excessive amounts of chaos and fractal complexity are harmful to biological predictions. For biological applications, chaos analysis may be advantageous. This paper is a review which highlights the fractal and chaos theories for biological functions and biomedical systems. The focus will be to explore biological functions, due to its computational machine learning-based demands and capability in mathematical complexity.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125661067","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

NetBIOS DDoS Attacks Detection With Machine Learning Classification Algorithms 用机器学习分类算法检测NetBIOS DDoS攻击

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141815

S. Mekala, K. Dasari

{"title":"NetBIOS DDoS Attacks Detection With Machine Learning Classification Algorithms","authors":"S. Mekala, K. Dasari","doi":"10.1109/InCACCT57535.2023.10141815","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141815","url":null,"abstract":"Distributed Denial of Service (DDoS) is a cyberattack in which the attacker makes a system or network resources unavailable to the intended audience temporarily or permanently. NetBIOS DDoS is a reflection based DDoS attack makes the victim system unavailable to communication other NetBIOS hosts. Service unavailable makes huge impact in terms of financially and reputational. So DDoS attack detection at early stage is more important. This study proposed machine learning algorithms for NetBIOS DDoS attack detection. Experiments are performed on NetBIOS_DrDoS dataset, which is collected from CIC-DDoS2019 evaluation dataset. In order to reduce computational overheads features are selected by Correlation methods. This study uses Pearson, spearman and Kendall correlation methods to select uncorrelated features. This study evaluated Logistic regression, Decision tree, Random forest, Ada Boost, Gradient Boost, K-Nearest Neighbour, Naive-Bayes and Multilayer perceptron classification algorithms with Pearson, Spearman and Kendall uncorrelated feature subsets in order to classify attack and benign class labels. Multilayer perceptron with Pearson uncorrelated feature subset gives the best performance for NetBIOS DDoS attack detection.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129256255","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

A Comparative Analysis of Image Classification Classifiers 图像分类器的比较分析

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141693

N. Jha, R. Popli

{"title":"A Comparative Analysis of Image Classification Classifiers","authors":"N. Jha, R. Popli","doi":"10.1109/InCACCT57535.2023.10141693","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141693","url":null,"abstract":"The most significant and difficult issue in computer vision is classification. The characterization, structure, or likeness of objects is used to classify them. The categorization of images into one of several designated groups is known as image classification. An image is expressed by units called pixels. A method called image classification interprets an image and derives data from it that could be applied to other tasks. Using Machine Learning (ML) techniques to address the complicated problem of image classification presents a challenge in today’s environment. The K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), ISODATA and Random Forest classifier methods are used to tackle these objectives in the study. In addition to outlining each technique’s benefits and drawbacks, this overview offers theoretical background on a range of classification methodologies. This study can aid in leveraging the efficiency of neural networks for classifying tasks that call for non-binary classifications, which is a common requirement when actual statistics is taken into account.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126282646","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

An In-depth Exploration of ResNet-50 for Complex Emotion Recognition to Unraveling Emotional States 复杂情绪识别ResNet-50对情绪状态解析的深入探索

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141774

T.N.V.S Praveen, Dasaradhi Sivathmika, Goparaju Jahnavi, Jaswitha Bolledu

{"title":"An In-depth Exploration of ResNet-50 for Complex Emotion Recognition to Unraveling Emotional States","authors":"T.N.V.S Praveen, Dasaradhi Sivathmika, Goparaju Jahnavi, Jaswitha Bolledu","doi":"10.1109/InCACCT57535.2023.10141774","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141774","url":null,"abstract":"Emotional Recognition is an important task in computer vision and has many applications, including humanrobot interaction, virtual assistants, and mental health monitoring. In recent years, deep learning models have shown great promise in accurately recognizing emotions from images and videos. This paper proposes the ResNet-50 model for emotional recognition. ResNet-50 is a popular deep learning architecture that has been successfully applied to image classification tasks, and can be adapted for emotional recognition. Using ResNet-50 for emotional recognition offers several advantages over existing approaches. Firstly, ResNet-50 is a well-established architecture that has been extensively used in many computer vision tasks, which makes it a reliable choice. Secondly, ResNet-50 has a deep architecture with many layers that allows it to capture more complex patterns and features from images, which can lead to better emotion recognition performance. Finally, ResNet-50 is relatively efficient in terms of computational resources, which means it can be trained and deployed on a variety of hardware, including low-power devices such as smartphones and embedded systems. ResNet-50 for emotional recognition has several advantages over existing approaches, including reliability, better performance, and efficient use of computational resources. These advantages make ResNet-50 a promising candidate for emotion recognition tasks in a wide range of applications.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404330","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

Digital Image Encryption Using 256-Bit Advanced Encryption Standard Algorithm 使用256位高级加密标准算法的数字图像加密

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141709

Jaideep Kala, J. Panda, Lavi Tanwar

引用次数: 0