2021 International Conference on Intelligent Technologies (CONIT)最新文献_第2页

Application of Linear Programming for Overcurrent Protection 线性规划在过流保护中的应用

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498330

P. S. Patil, A. Badar, Trupti P. Hinge

引用次数: 1

Time-Borrowing Flip-Flop Architecture for Multi-Stage Timing Error Resilience in DVFS Processors 用于DVFS处理器多阶段时序误差恢复的借时触发器结构

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498379

Avisekh Ghosh, Mohd Saif Naseem, C. I. Kumar

{"title":"Time-Borrowing Flip-Flop Architecture for Multi-Stage Timing Error Resilience in DVFS Processors","authors":"Avisekh Ghosh, Mohd Saif Naseem, C. I. Kumar","doi":"10.1109/CONIT51480.2021.9498379","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498379","url":null,"abstract":"Near-threshold voltage (NTV) design has been proposed for low-power VLSI designs across a wide range of applications due to its optimal Energy-Delay Product. Dynamic Voltage and Frequency Scaling (DVFS) design has acquired recent interest to increase energy efficiency during runtime. During periods of low utilization, a chip’s performance and power consumption can be adjusted by slowing down the clock speed and decreasing the supply voltage, thereby bringing its operation to the NTV region. However, high dynamic variations at low voltage operation is the key design challenge and barrier for near-threshold adoption. Hence, it becomes mandatory to consider large timing margins during design to ensure high yield and robust operation. Error resilient circuits help to restore the timing margins, thereby improving performance with low power consumption. This paper presents EDTB (Error Detection with Time-Borrowing), a novel architecture for multi-stage timing error resilience which masks timing errors by borrowing time from successive pipeline stages, without slowing down the clock speed. The proposed error handling scheme doesn’t require error recovery mechanisms like instruction replay or roll-back; hence it doesn’t associate any error recovery penalty. EDTB-based error masking is resilient to glitches and spurious transitions due to its minimal hold time requirement. EDTB architecture shows a significant improvement of up to 34% in transistor count and 50% in clock routing over the previous architecture. The circuit schematic was prototyped in Cadence Virtuoso ADE in a 7-nm FinFET based predictive process design kit; followed by simulation, validation, and further comparisons.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124688692","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

Model for IaaS Security Model: MISP Framework IaaS安全模型:MISP框架

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498375

Indra Kumar Sahu, M. Nene

{"title":"Model for IaaS Security Model: MISP Framework","authors":"Indra Kumar Sahu, M. Nene","doi":"10.1109/CONIT51480.2021.9498375","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498375","url":null,"abstract":"Cloud computing offers plenty of services having benefits of cost cutting, flexibility, availability, elasticity and pay per use for cloud users and centralized control, safety and management for Cloud Service Providers (CSP). Out of several service models, Infrastructure-as-a-Service (IaaS) preludes by offering access to computing resources like CPUs, servers, network devices through software like APIs, UIs and GUIs over the internet. Along with numerous merits of IaaS, there exist several security and privacy issues and threats to Confidentiality, Integrity and Authentication (CIA) triad. In this paper, we explore security and privacy issues related to IaaS components through rigorous study and determine counter measures for the same. Furthermore, with the help of MITRE ATT&CK knowledge base, a Model for IaaS Security and Privacy: MISP framework is proposed. The framework is exhibited as a multidimensional structure, each dimension having different parameters. This leads to addition of important features currently missing in the existing models. The proposed framework enhances security and privacy in IaaS model and guides for safer adoption of the delivery model by organizations and enterprises.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123336987","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: 7

Secure Cloud Data Deduplication with Efficient Re-Encryption 安全的云数据重复删除与高效的重新加密

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498487

Mohd Aman, P. Verma, D. Rajeswari

{"title":"Secure Cloud Data Deduplication with Efficient Re-Encryption","authors":"Mohd Aman, P. Verma, D. Rajeswari","doi":"10.1109/CONIT51480.2021.9498487","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498487","url":null,"abstract":"After the emergence of the cloud architecture, many companies migrate their data from conventional storage i.e., on bare metal to the cloud storage. Since then huge amount of data was stored on cloud servers, which later resulted in redundancy of huge amount of data. Hence in this cloud world, many data de-duplication techniques has been widely used. Not only the redundancy but also made data more secure and privacy of the existing data were also increased. Some techniques got limitations and some have their own advantages based on the requirements. Some of the attributes like data privacy, tag regularity and interruption to brute-force attacks. To make data deduplication technique more efficient based on the requirements. This paper will discuss schemes that brace user-defined access control, by allowing the service provider to get information of the information owners. Thus our scheme eliminates redundancy of the data without breaching the privacy and security of clients that depends on service providers. Our lastest deduplication scheme after performing various algorithms resulted in conclusion and producing more efficient data confidentiality and tag consistency. This paper has discussion on various techniques and their drawbacks for the effectiveness of the deduplication.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123650395","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

Extractive Text Summarization Using Supervised Learning and Natural Language Processing 使用监督学习和自然语言处理的抽取文本摘要

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498322

Sarita Mandal, P. Achary, Shubhada Phalke, K. Poorvaja, Madhuri Kulkarni

{"title":"Extractive Text Summarization Using Supervised Learning and Natural Language Processing","authors":"Sarita Mandal, P. Achary, Shubhada Phalke, K. Poorvaja, Madhuri Kulkarni","doi":"10.1109/CONIT51480.2021.9498322","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498322","url":null,"abstract":"The amount of textual data that we are exposed to is growing each day. It is very difficult to browse through all the available textual matter to find relevant material or to read through all the information in order to stay updated. To keep up with the pace, the need for a tool that can automatically reduce the amount of content while also retaining the key points and essence of long pieces of text arises. Automatic text summarization mechanisms form a solution well suited to this problem which is what our proposed model aims to implement. In this paper, a Natural Language Processing based extractive approach is used for summarization of a single document. An extractive summary is assembled by selection of a subset of information rich sentences from the source document. A supervised approach is used here in which Support Vector Machine, K-Nearest Neighbour and Decision Tree algorithms are used to generate models whose performances are compared using ROUGE metric. The highest scoring model is used to summarize an unseen document. The summary is displayed as text and converted to audio form. The results obtained using the proposed approach are sufficiently good as average F1 scores secured for ROUGE-1, ROUGE-2 and ROUGE-L are 0.706, 0.630 and 0.434 respectively.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121195343","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

Deep Learning based Detection of Water Bodies using Satellite Images 基于深度学习的水体卫星图像检测

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498442

Deepa Gupta, Vaibhav Kushwaha, Akarth Gupta, P. K. Singh

{"title":"Deep Learning based Detection of Water Bodies using Satellite Images","authors":"Deepa Gupta, Vaibhav Kushwaha, Akarth Gupta, P. K. Singh","doi":"10.1109/CONIT51480.2021.9498442","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498442","url":null,"abstract":"A lot of ongoing developments are going on in the field of Deep Learning and then further in Image Processing. This paper aims to focus on the processing done using Convolutional Neural Networks to obtain the images having water pixels classified appropriately. The identification of water bodies and the knowledge about the geography of those regions is crucial to a lot of activities, it helps in further planning the developments in that region and in emergency operations too i.e. in rescue operations. The images are basically obtained through remote methods or by using low flying drones that capture them. However, in addition to the cost, following issues must be considered: remote satellite images may not trace sudden changes over a particular latitude and longitude while the drone may take a lot of time to capture all the details. The whole objective of this research is to find the locations of water bodies using the data available through images and then finding the area or region over which they are spread. The satellite images from Sentinel-2 have been used and the shape files too have been obtained in order to map the initial training data for the model. The mapped data is then stored in the form of preprocessed result and the model is trained further using the the preprocessed data. The time taken for the processing of the input images and the shapefiles depends highly on the machine being used, low end machines might crash while opening a shapefile because the size of the shapefile might go upto 1.5 GigaBytes. Eventually the whole process resulted into a trained classifier with an accuracy greater than 90% and with 70% precision.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404224","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

Efficient Methods to Improve the Performance of Supervised Learning Models 改进监督学习模型性能的有效方法

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498387

Mohit Kumar, Pragya Yadav, H. Singh, Ankita Arora

{"title":"Efficient Methods to Improve the Performance of Supervised Learning Models","authors":"Mohit Kumar, Pragya Yadav, H. Singh, Ankita Arora","doi":"10.1109/CONIT51480.2021.9498387","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498387","url":null,"abstract":"Supervised Learning is defined as training a model with input data that includes the result itself. There are large number of supervised learning algorithms and great number of models. Each model has its own merits and demerits and performs differently. There are many data preprocessing techniques and hence the combination of several data preprocessing techniques can increase the performance of the present supervised learning models. Primary data can not be fed directly to the learning model because it can hold a lot of noise. It needs to be preprocessed using various data preprocessing techniques. We have analysed and compared different data preprocessing techniques and their combinations. Comparison is done using various performance metrics and the combination of different data preprocessing is applied to different model. We have done categorical data handling, missing value treatment, feature scaling and feature extraction as the data preprocessing steps. Through the comparison, we found which technique is better for which type of models. We used the California census data for our study.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116156588","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 Solution to Covid-19: Detection and Recognition of Faces with Mask Covid-19的解决方案:戴口罩的人脸检测和识别

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498426

V. Nithyashree, Roopashree S, Aparna Duvvuri, L. Vanishree, Disha Anand Madival, G. Vidyashree

引用次数: 1

K factor-based MPPT Technique for Reducing Steady-State Power Oscillations 基于K因子的MPPT减小稳态功率振荡技术

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498521

Mayank Arora, C. Vyjayanthi

引用次数: 1

Performance of Solar Panel in Presence and Absence of Dust 太阳能电池板在有尘和无尘条件下的性能

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498533

Snehal A. Marathe, B. Patil

引用次数: 4