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2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)最新文献

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Comparative study of approximation using Lagrange and Hermite form of polynomial interpolations 多项式插值的拉格朗日逼近与厄米特逼近的比较研究
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397190
Shashwati Ray, Vandana Chouhan
{"title":"Comparative study of approximation using Lagrange and Hermite form of polynomial interpolations","authors":"Shashwati Ray, Vandana Chouhan","doi":"10.1109/ICCCIS51004.2021.9397190","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397190","url":null,"abstract":"Interpolation means replacing complicated function by simple function which matches at distinct nodes in given interval. Best approximation depends on choice of calculating method, number of nodes and type of nodes. In present work comparative study is done between Lagrange and Hermite interpolation with equally spaced and Chebyshev nodes, by approximating four functions of different property. Experiments have shown that the Hermite polynomial with Chebyshev node distribution gives result of higher accuracy than those obtained using equally spaced nodes.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117152271","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
Comparison among different CNN Architectures for Signature Forgery Detection using Siamese Neural Network 基于Siamese神经网络的不同CNN体系结构签名伪造检测的比较
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397114
Soumya Jain, M. Khanna, Ankita Singh
{"title":"Comparison among different CNN Architectures for Signature Forgery Detection using Siamese Neural Network","authors":"Soumya Jain, M. Khanna, Ankita Singh","doi":"10.1109/ICCCIS51004.2021.9397114","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397114","url":null,"abstract":"Signature is the most common way to indicate knowledge and acceptance of a document. As many documents and contracts are now starting to use paperless electronic formats, the term \"signature\" has been substantially broadened. Whichever form it takes, the key importance of the signature is identity authentication for managing security. Signatures being one of the most widely used methods for the same, play a crucial role in financial, legal, and social aspects of one's life. Thus, Signature forgery, that is falsely copying another individual’s signature is an issue of utmost concern. The chances of two or more signatures made by the same individual being identical are minimal, thus making signature forgery detection an arduous task. Our paper aims to apply the state-of-the-art methodology, Siamese Neural Networks, on the chosen data set, draw meaningful insights and perform a comparative analysis between some variants of these neural networks to identify and authenticate handwritten signatures.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116165548","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: 6
Analysis of LiFi System Performance Considering Different Bit rates and Link Ranges 考虑不同比特率和链路范围的LiFi系统性能分析
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397088
Gaurja Bahl, Yatin Singh Sammal, Sanmukh Kaur, Nivedita Nair
{"title":"Analysis of LiFi System Performance Considering Different Bit rates and Link Ranges","authors":"Gaurja Bahl, Yatin Singh Sammal, Sanmukh Kaur, Nivedita Nair","doi":"10.1109/ICCCIS51004.2021.9397088","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397088","url":null,"abstract":"With the growth in population, the number of devices connected to internet are expected to increase exponentially. The current wireless communication system which is based on radio frequency (RF) technology, is not able to satisfy the requirement of high speed data consumption due to limited spectrum availability and network congestion. In this paper we analyse the performance of LiFi communication system by employing an LED with 450 nm wavelength as optical source for communication using RZ-OOK modulation format. Performance of the system has been evaluated through Q factor and BER values for different bit rates and link ranges. The system can support 50 Mbps data rate upto 3.4 m of link range with a Q Factor of 10.71.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125966277","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
Image Steganography based on Fractional Random Wavelet Transform and Arnold Transform with cryptanalysis 基于分数阶随机小波变换和阿诺德变换的图像隐写与密码分析
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397107
U. Sivaramakrishnan, Namrata Panga, G. K. Rajini
{"title":"Image Steganography based on Fractional Random Wavelet Transform and Arnold Transform with cryptanalysis","authors":"U. Sivaramakrishnan, Namrata Panga, G. K. Rajini","doi":"10.1109/ICCCIS51004.2021.9397107","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397107","url":null,"abstract":"Steganography is a means to hide information which is mainly confidential in order to avoid leakage of important data. In medical diagnosis it is necessary to hide the medical records of the patients on moral grounds. This research paper applies image steganography using Fractional Random Wavelet Transform (FrRnWT). Using this transform steganography is achieved by embedding the secret image onto a cover image. We make a comparison with another transform, Discrete wavelet transform (DWT), a frequently used to transform images in image steganography. To further increase the security of the information we use Arnold scrambling algorithm to prevent any unauthorized access of information. The performance is analyzed by the computing the following parameters: PSNR and MSE and exposing the system to attacks to compare their imperceptibility and robustness. The results are shown to observe the effectiveness of the proposed method.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129448210","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 forecasting of Time-Series data using S-ARIMA, CNN and LSTM 基于S-ARIMA、CNN和LSTM的时间序列数据分析与预测
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397134
Subhash Arun Dwivedi, Amit Attry, Darshan Parekh, Kanika Singla
{"title":"Analysis and forecasting of Time-Series data using S-ARIMA, CNN and LSTM","authors":"Subhash Arun Dwivedi, Amit Attry, Darshan Parekh, Kanika Singla","doi":"10.1109/ICCCIS51004.2021.9397134","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397134","url":null,"abstract":"Analyzing the behavior of stock market movements has often been an area of interest to machine learning and time-series data analyst. It has been very challenging due to its immense complex nature, chaotic, and dynamic environment. With the advent of machine learning and deep learning algorithms, this paper aims to significantly reduce the risk of trend prediction. This study compares models for Time – Series forecasting i.e. SARIMA (Seasonal Auto-Regressive Integrated Moving Average), CNN (Convolutional Neural Network), and LSTM (Long Short-Term Memory) for predicting Nifty-500 indices trend. The results that were obtained are promising and the evaluation unveils the power of Deep Learning through CNN and LSTM but also empowers the S-ARIMA model, making a great impact on the Machine Learning paradigm.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787767","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: 6
AI Optics: Object recognition and caption generation for Blinds using Deep Learning Methodologies 人工智能光学:使用深度学习方法的盲人对象识别和标题生成
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397143
Moksh Grover, Rajat Rathi, Chinkit Manchanda, K. Garg, R. Beniwal
{"title":"AI Optics: Object recognition and caption generation for Blinds using Deep Learning Methodologies","authors":"Moksh Grover, Rajat Rathi, Chinkit Manchanda, K. Garg, R. Beniwal","doi":"10.1109/ICCCIS51004.2021.9397143","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397143","url":null,"abstract":"With the exponential development in the field of artificial intelligence in recent years, many researchers have focused their attention towards the topic of image caption generation. With this topic being that of arduous task and interest people take it as a challenge to perform to excel in the field of AI. Automatic generation of neutral language descriptions or ‘captions’ according to the composition detected in an image, i.e., scene understanding is the main part of image caption generation which can be achieved by combining both natural language processing along with computer vision. In this paper, we tackle the task of generating captions by using the concepts of Deep Learning.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128189100","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
Sentiment Analysis using Feature Generation And Machine Learning Approach 基于特征生成和机器学习方法的情感分析
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397135
Roopam Srivastava, P. Bharti, Parul Verma
{"title":"Sentiment Analysis using Feature Generation And Machine Learning Approach","authors":"Roopam Srivastava, P. Bharti, Parul Verma","doi":"10.1109/ICCCIS51004.2021.9397135","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397135","url":null,"abstract":"The study of opinion offers answers to what the most critical problems are. Since sentiment analysis can be automated, judgements can be taken based on a significant amount of data rather than plain intuition, which is not always accurate. This paper focuses on the feature generation using Bag-of-Words and TF-IDF and the build model using the machine learning approach for sentiment analysis. The dataset used contains review of trip advisor on various hotels. This dataset consists of 20k reviews. Word cloud had been formed using sentiment ratings. Data was cleaned and pre-processed, and then applied Bow and TF-IDF for feature extraction. After implementation of classifiers, training and evaluation was performed. Evaluation metrics is used for measuring the accuracy of classifier. MultinomialNB obtained the highest accuracy in the realm of Bag of Word features and random forest outperformed in the case of TF-IDF out of three classifiers used to determine accuracy. We got 82% of the classification rate of MultinomialNB in Bag of Word and 78% accuracy in TF-IDF Random Forest.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128197968","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
English to Nepali Sentence Translation Using Recurrent Neural Network with Attention 带注意的递归神经网络英汉互译
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397185
Kriti Nemkul, S. Shakya
{"title":"English to Nepali Sentence Translation Using Recurrent Neural Network with Attention","authors":"Kriti Nemkul, S. Shakya","doi":"10.1109/ICCCIS51004.2021.9397185","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397185","url":null,"abstract":"Machine Translation, an automated system that intakes the text from the source language as an input, applies some computation on that input and gives the equivalent text in the target language without any human involvement. This research work focuses on developing the models for English to Nepali sentence translation incorporating Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) with attention. Bilingual Evaluation Understudy (BLEU) Score is calculated to evaluate the efficiency of the model. Different parameters has been used to test the model. The model has been tested with neural network layer 2 and 4 and with hidden units 128, 256 and 512. The GRU cells in encoder and decoder with attention with 2 layer of neural network and 512 hidden units appears to be better in translating the English sentences into Nepali sentences with highest BLEU score 12.3.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128232994","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
An Ensemble Approach to Multi-Source Transfer Learning for Air Quality Prediction 空气质量预测中多源迁移学习的集成方法
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397138
Aditya Dhole, Ishan Ambekar, Gaurav Gunjan, S. Sonawani
{"title":"An Ensemble Approach to Multi-Source Transfer Learning for Air Quality Prediction","authors":"Aditya Dhole, Ishan Ambekar, Gaurav Gunjan, S. Sonawani","doi":"10.1109/ICCCIS51004.2021.9397138","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397138","url":null,"abstract":"Declining quality of air is one of the major challenges faced by cities across the globe today. A product of rapid industrialization and urbanization, air pollution has quickly manifested itself as an existential threat to human life in major urban centres of the world. Developing economies such as India and China are at the forefront in bearing the brunt of this phenomenon. Intelligent predictive analysis is critical for framing policies that can help in controlling the severe effects of air pollution. Although Deep Learning architectures have proven to be effective in reliably forecasting the concentration of hazardous air particles like PM2.5, their predictive capabilities are significantly reduced when the amount of data available is not enough for effective training. Newer monitoring stations often lack the resources and/or personnel to reliably collect meteorological and environmental data, thus suffering from crippling data-insufficiency issues which hinder the applicability of forecasting models. This paper proposes an ensemble approach for Multi-Source Transfer Learning, with the aim of mitigating the data shortage issue. The proposed method generates a cumulative prediction by transferring the knowledge learned from multiple source stations to a given target station, thereby better utilizing the data that is readily available from nearby stations to boost prediction performance.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127065523","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: 5
Augmented Reality in Medical Education: AR Bones 医学教育中的增强现实:AR骨骼
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Pub Date : 2021-02-19 DOI: 10.1109/ICCCIS51004.2021.9397108
Mohammad Fahim Hossain, Sudipta Barman, Niloy Biswas, A. K. M. Bahalul Haque
{"title":"Augmented Reality in Medical Education: AR Bones","authors":"Mohammad Fahim Hossain, Sudipta Barman, Niloy Biswas, A. K. M. Bahalul Haque","doi":"10.1109/ICCCIS51004.2021.9397108","DOIUrl":"https://doi.org/10.1109/ICCCIS51004.2021.9397108","url":null,"abstract":"Augmented Reality is a technique that enhances reality with computer-generated imagery, three-dimensional objects, or other information that doesn’t physically exist in the real world. It is a growing technology that offers promising potentials in terms of learning and training. This paper focuses on the development process of a mobile-based application utilizing Mobile Augmented Reality. Since there are vast amounts of information on the human skeleton to comprehend, anyone would find it challenging to retain all the details. This application aims to aid students as a supplement in human anatomy studies. Thus, this project is an approach to enhance their current learning processes with the help of Mobile Augmented Reality.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741671","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: 6
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