2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)最新文献_第2页

Fast Food Image Recognition using Transfer Learning 快餐图像识别使用迁移学习

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058675

Arnav A Rajesh, Madhumita Raghu, J. Sangeetha

引用次数: 0

A Review of Benchmark Datasets and its Impact on Network Intrusion Detection Techniques 基准数据集综述及其对网络入侵检测技术的影响

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058660

H. C., Prabhudev Jagadeesh M.P.

{"title":"A Review of Benchmark Datasets and its Impact on Network Intrusion Detection Techniques","authors":"H. C., Prabhudev Jagadeesh M.P.","doi":"10.1109/CCIP57447.2022.10058660","DOIUrl":"https://doi.org/10.1109/CCIP57447.2022.10058660","url":null,"abstract":"With the advancements in Internet technology and emergence of new devices and network architectures, Cyber-attacks are becoming more sophisticated. As always with new technology, comes new threats. The main challenge is to accurately detect these intrusions and alert the network administrator to prevent these attacks. Deep learning techniques have proven to be more effective when compared to shallow machine learning methods. Intrusion detection can be classified into several types based on architecture, implementation, algorithm type etc. Dataset plays a vital role in the performance analysis of the model. Quality of the dataset adversely affects the training and would to lead to incorrect and inconsistent model results. The availability of datasets for conducting experiments for IDSs (Intrusion Detection Systems) continues to be a problem. Many publicly available datasets do not always accurately reflect data from the real world. On the other hand, because they are made available to the public, they make it possible for researchers to do similar benchmarking. On the basis of self-generated datasets, some experiments are being conducted. They may be better suited to a particular study group's needs, although privacy issues may arise. This paper discusses in detail, the different datasets used in Network Intrusion Detection. Different metrics that can be used in evaluating intrusions are also discussed. Impact of the datasets in model accuracy, challenges and future directions in carrying out research in Network intrusion detection are also addressed.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125239377","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

IVF Success Rates Prediction Using Hybrid ANN-GA based Machine Learning Model 基于ANN-GA混合机器学习模型的体外受精成功率预测

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058652

Gowramma G S, Shantharan Nayak, Nagaraj G Cholli

{"title":"IVF Success Rates Prediction Using Hybrid ANN-GA based Machine Learning Model","authors":"Gowramma G S, Shantharan Nayak, Nagaraj G Cholli","doi":"10.1109/CCIP57447.2022.10058652","DOIUrl":"https://doi.org/10.1109/CCIP57447.2022.10058652","url":null,"abstract":"Machine learning techniques have been studied with the aim of improving the predictions of In Vitro fertilization (IVF) Live Birth occurrence rates by assessing the extrinsic and intrinsic parameters that principally influence IVF process. Predictive performance of the machine learning techniques is directly related to the quality of the training database and also on the set of hyperparameters screened in the prediction process. Obtaining the best hyperparameters is not a trivial task, but can be achieved by implementing bioinspired algorithms such as Artificial neural network (ANN) and Genetic Algorithms (GA). ANN-GA hybrid design works based on the natural selection theory and evolve the solutions that produce good hyperparameters for Machine learning techniques to register higher accuracy predations Predictions. The IVF/ANN-GA has the aim to improve the performance of hybrid machine learning design with the addition of IVF-Inspired mechanisms that better exploit the information of individuals. With this aim, the present study explores the combination of an ANN with GA to search for the best set of hyperparameters to predict the success rates of the process. The results supported with high accuracy, precision, and recall. Performance values of the model such as F1-measure precision 0.85, recall values 0.76, F1_score 0.80 and accuracy measure 0.89 were noted. The measured values indicate that the model applied exhibits the true positive detection rate of 85%. Models detecting with false positives chance is measured to be only 15%. Study concludes that, present investigation rely both on precision and recall and which were successfully considered in the study metrics. F1 score of the employed design explains the arithmetic ratio of both precision and recall with 89% value. The present studied ANN-GA hybrid model achieved the overall accuracy rates of 90% in predicting the IVF Live Birth rates measures.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850826","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

FPGA based Design and Simulation of basic Routing Protocols 基于FPGA的基本路由协议设计与仿真

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058623

Sumana Achar, D. Jayadevappa

引用次数: 0

Storage Virtualization Mechanism for Securing Electronic Health Records in Cloud 云环境下电子健康记录安全的存储虚拟化机制

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058624

Ramana Reddy B, I. M

引用次数: 0

Gas Leakage and Smoke Detection System in Geyser using Arudino Nano 基于Arudino Nano的间歇泉气体泄漏和烟雾检测系统

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058686

R. Amulya, YJ Harshitha, N. Pallavi, M. Dakshyani

引用次数: 0

Compression of High – Resolution Medical and Space Color Video using Butterworth Filter 利用巴特沃斯滤波器压缩高分辨率医学和空间彩色视频

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058648

A. Patra, Debasish Chakraborty, Sonali Sarkar, Subhashis Kar

引用次数: 0

Artificial Neural Networks in Healthcare for Augmented Reality 增强现实医疗保健中的人工神经网络

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058670

Pradeepa K, B. N, Meenakshi D, H. S, Kathiravan M, Vinoth Kumar

{"title":"Artificial Neural Networks in Healthcare for Augmented Reality","authors":"Pradeepa K, B. N, Meenakshi D, H. S, Kathiravan M, Vinoth Kumar","doi":"10.1109/CCIP57447.2022.10058670","DOIUrl":"https://doi.org/10.1109/CCIP57447.2022.10058670","url":null,"abstract":"Deep learning (DL) techniques like recurrent neural networks (RNN) and convolutional neural networks (CNN) are currently being utilised to improve management tooling and workflow classification to increase operational effectiveness. Reliability could be increased, but because of CNN's intricacy, actual research is therefore limited. A brand-new DL structure is suggested in this study to incorporate the visualization of mappings (IVM) within Masked R-CNN. During the first approach, this paradigm, IVM-CNN combines the best features of both approaches, including (1) IVM for object tracking by emphasizing geospatial data for sector recommendations and (2) CNN for machine vision by relying on data for picture categorization. Using spatial and temporal statistics along with visual functionalities, the said approach is tested on M2CAI 2016 contest sets of data, outperforming all prior creations and accomplishing futuristic outcomes to 97.1 mAP for device diagnosis and 96.9 mean rate. It also performs at 50 FPS, which is ten times quicker than region-based CNN. Masked R-CNN substitutes the region proposal network (RPN) with a region proposal module (RPM), which more precisely generates boundary boxes and reduces the demand for labeling. Microsoft HoloLens software is also being generated to offer an augmented reality (AR) stationed approach for clinical education and help.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121099473","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

Automatic SWT Based QRS Detection Using Weighted Subbands and Shannon Energy Peak Amplification for ECG Signal Analysis Devices 基于加权子带和Shannon能量峰值放大的自动SWT QRS检测在心电信号分析设备中的应用

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058632

Jomole Varghese V, M. Manikandan, R. B. Pachori

引用次数: 2

A reliable solution to detect deepfakes using Deep Learning 使用深度学习检测深度伪造的可靠解决方案

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI: 10.1109/CCIP57447.2022.10058638

H. K. Vedamurthy, R. V, Gururaj S P

{"title":"A reliable solution to detect deepfakes using Deep Learning","authors":"H. K. Vedamurthy, R. V, Gururaj S P","doi":"10.1109/CCIP57447.2022.10058638","DOIUrl":"https://doi.org/10.1109/CCIP57447.2022.10058638","url":null,"abstract":"Recently, it has become simple to produce trustworthy face video exchanges that leave a few signs of deception thanks to in-depth free reading software tools (DF). Despite decades of effective use of visual effects in digital video deception, recent developments in in-depth learning have significantly improved the genuine nature of misleading content and the accessibility that can be achieved with it. This is referred to as AI-synthesized media or DF in short. Making DF is a simple task that uses practical tools. However, it is a significant difficulty if these DFs are discovered, because it is hard to train the algorithm for identifying DF. CNNs and RNNs have helped us come closer to DF. The Convolutional Neural Network (CNN) is used by the system to extract features at the individual level. The continuous neural network (RNN) states learn to recognize whether or not a video is being deceived and be able to spot temporary anomalies among the frames given by DF's creative tools thanks to these capabilities. An extensive collection of pseudo-videos gathered from a common data source is the anticipated outcome. We demonstrate how our method can produce a competitive outcome in this work that is simple to utilize.","PeriodicalId":309964,"journal":{"name":"2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"600 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134462780","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