2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)最新文献

Security Flaws in Dhillon and Kalra's User Authentication Scheme for IoT Dhillon和Kalra的物联网用户认证方案中的安全漏洞

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064577

P. Tyagi, S. Kumari

引用次数: 0

Drowsiness Detection of Driver 驾驶员睡意检测

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064575

B. Jyothi, Karthik Seethina, P. Bhavani, Chenna Jayanth

{"title":"Drowsiness Detection of Driver","authors":"B. Jyothi, Karthik Seethina, P. Bhavani, Chenna Jayanth","doi":"10.1109/ICICT55121.2022.10064575","DOIUrl":"https://doi.org/10.1109/ICICT55121.2022.10064575","url":null,"abstract":"During this fast-paced developing world we want advanced procedures to spot the real time methods to identify to save a life, that is valuable in saving a family from negatives if road accidents occur. This paper justifies the hazards on road that happen due to driver being drowsy. Previous studies conclude more hazards being created due respect of drowsiness. This project articulates different types involved in identifying the driver condition and warns the person. The 2 ways we can identify the state of driver at wheel is by using following techniques. First is dependency of psychology whereas other is based on behaviour. In continuous detection tech world, driver exhaustion acknowledgment is one amongst important business. We discuss the driver is alerted based on the response from face. In regard with this Machine Learning the subtopic in AI i.e., computing is employed in specified way of predicting state of a person to generate data which tend to increase the Ideology of “safety first” on the highways and road. AI could be a system that is having capacity to adapt to new learning by continuously improving without requiring the need to modify or adapt to the new technology and the programs. during this paper we include literature survey of previous studies with respect to person drowsiness detection and attention buying technology. We adapt to learn the Perclos or Euclidian algorithm, cascade classifier based on haar, OpenCV, Python that are crucially employed to detect the driver. At last, we undergo the future study and scope with regarding to advancements on the study with particular project.","PeriodicalId":181396,"journal":{"name":"2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694510","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

Analysis and Classification of Restaurants Based on Rating with XGBoost Model 基于XGBoost模型的饭店评级分析与分类

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064491

Anuj Kumar Dixit, Rekha R Nair, T. Babu

{"title":"Analysis and Classification of Restaurants Based on Rating with XGBoost Model","authors":"Anuj Kumar Dixit, Rekha R Nair, T. Babu","doi":"10.1109/ICICT55121.2022.10064491","DOIUrl":"https://doi.org/10.1109/ICICT55121.2022.10064491","url":null,"abstract":"The restaurant business is one of the most competitive and the need for restaurants is growing daily. Bangalore is a foodie's paradise, boasting cuisines from all over the world. Hence, this research work focuses the classification of restaurants on the basis of rate with XGBoost model. The Exploratory Data Analysis(EDA) with different graphs provides an analysis of the data before classification. Prior to EDA the data sets are cleaned with various steps to increase the accuracy of the visualization and classification. The research was performed using Zomato data set for restaurants in a specific locality(Bangalore). Data Visualization techniques helped to analyze food culture, trends and patterns. This research describes a model for understanding the elements that influence restaurant ratings. Predictive analytics and machine learning together with a variety of tools and methodologies, help in predicting restaurant ratings. The model in this research is developed using multiple supervised techniques, with the most efficient algorithm being evaluated. The classification XGBoost model provided an accuracy of 98.07 %. The outcome of the work assists new restaurants in selecting on their menu, cuisine, theme, prices, geographic location, and so on, consequently enhancing business. The rate and location details helps people to select the restaurants for the dining.","PeriodicalId":181396,"journal":{"name":"2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"16 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129855983","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

Generative Adversarial Networks, Their Various Types, A Comparative Analysis, and Applications in Different Areas 生成对抗网络，它们的各种类型，比较分析，以及在不同领域的应用

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064488

Dipanshi Singh, Anil Ahlawat

{"title":"Generative Adversarial Networks, Their Various Types, A Comparative Analysis, and Applications in Different Areas","authors":"Dipanshi Singh, Anil Ahlawat","doi":"10.1109/ICICT55121.2022.10064488","DOIUrl":"https://doi.org/10.1109/ICICT55121.2022.10064488","url":null,"abstract":"GANs(Generative Adversarial Networks) are two neural networks that compete with each other to develop new data which hasn't been there originally. If we train the GAN on images, the resultant will be imaged with realistic characteristics. A GAN consists of two sub-models in which is called a generator model and the other model called the discriminator model. The Generator generates the data from the input data. The Discriminator checks or distinguishes whether the data is a sample from the real world or a generated one. This model was given for the unsupervised learning method, but it can also be applied to semi-supervised models. In this paper, we present a survey on GANs. First, we explain what simply generative adversarial networks(GANs) are, what are the different types of GANs, then what are the applications of generative adversarial networks(GANs) and then we explain the different areas in which GANs are being used and lastly what future GANs hold in different areas or fields or how can we use GANs in technology and research as a future evolving aspect.","PeriodicalId":181396,"journal":{"name":"2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321954","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 Study of Deep Learning and Machine Learning Algorithm for Sentiment Analysis 情感分析中深度学习与机器学习算法的比较研究

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064544

Ayush Agarwal, S. Meena

{"title":"A Comparative Study of Deep Learning and Machine Learning Algorithm for Sentiment Analysis","authors":"Ayush Agarwal, S. Meena","doi":"10.1109/ICICT55121.2022.10064544","DOIUrl":"https://doi.org/10.1109/ICICT55121.2022.10064544","url":null,"abstract":"Sentiment analysis is a classification procedure where we apply machine learning and deep learning algorithms to analyze the sentiment of the dataset, which consists of text, e.g., a message that can be of positive or negative sentiment. In this study, an attempt has been made to investigate which sentiment analysis techniques are feasible for product reviews. Here, the Amazon reviews dataset is used to compare, train, and test various machine learning and deep learning methods having product reviews from Amazon, which are chosen randomly from an open-source repository. The dataset comprises 4 million reviews. Comparison of several algorithms' performances, i.e., RFC, XGBC, LGBM, MNB, GBC, DTC, and Bi-LSTM, amongst which Bi-LSTM gives the highest performance among the algorithms used for classification. It was also applied to the other reviews from the Amazon dataset to predict the sentiment of the reviews, as well as a fresh Amazon scraped dataset comprising product reviews from several categories. This resulted in a very accurate classification, with the best results for test reviews on the amazon dataset. In conclusion, Bi-LSTM networks are excellent for categorizing customer sentiment on product reviews, and the results do not differ considerably across categories.","PeriodicalId":181396,"journal":{"name":"2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131415976","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

Mime Recognition for Indian Sign Language 印度手语的哑剧识别

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064616

Shrivarshaa Sakhamuri, Koppula Praneeta, Pidugu Jahnavi, Anuradha Chinta

引用次数: 0

Classification of Various Diseases Affecting Apple, Cherry, and Peach Plants using Modified VGGNet19 Architecture 基于改进VGGNet19结构的苹果、樱桃和桃子病害分类

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064586

Sanika Kapoor, Dipanshu Kumar, A. Sinha

引用次数: 1

Simulation and Comparative Analysis of Dynamic Comparators at 1MHz for Biomedical Applications 生物医学应用1MHz动态比较器的仿真与比较分析

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064520

Mohit Tyagi, P. Mittal, Parvin Kumar

{"title":"Simulation and Comparative Analysis of Dynamic Comparators at 1MHz for Biomedical Applications","authors":"Mohit Tyagi, P. Mittal, Parvin Kumar","doi":"10.1109/ICICT55121.2022.10064520","DOIUrl":"https://doi.org/10.1109/ICICT55121.2022.10064520","url":null,"abstract":"SAR ADC is one of the most demanding Analog to digital converter for medium speed, medium resolution applications like ECG, EEG, and related biomedical applications. In this paper we have designed and simulated the dynamic comparators with noise rejection and amplification capability in CADENCE Virtuoso 45 nm technology node. Designed Comparators are simulated at 0.8V and various parameters like power dissipation, maximum operating frequency, delay and offset voltage are compared. Designed comparators are well employable in SAR ADC with maximum operating frequency of 1MS/s. comparators are operated at varying supply voltage from 0 to 1 V. Basic comparator dissipates 9.17 pW as static power and 520.2 nW as dynamic power at VDD of 1 V and INN, INP difference of 0.9 V. Compared to basic comparator, dynamic comparator with tail transistors can be operated at 0.5 V with static power of 4.143 pW and 3.720 nW as dynamic power dissipation. Operating frequency of designed comparators is of 1 MHz with propagation delay of 522 ns by dynamic comparator with tail transistors and 480 ns as of basic comparator.","PeriodicalId":181396,"journal":{"name":"2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"21 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506636","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

Handwritten Mathematical Equation Recognition and Solver 手写数学方程识别和求解器

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064565

Riya Gupta, Y. Deshpande, Manasi Kulkarni

引用次数: 0

Automatic Bug Triaging Analysis using Machine Learning Techniques: A Review

2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2022-11-11 DOI: 10.1109/ICICT55121.2022.10064589

Rishabh Sirohi, Priya Singh

引用次数: 0