Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702577
Nikunj Bansal, Goutam Datta, Ashutosh Kumar Singh
Today’s Artificial Intelligence (AI) is data centric. Unlike earlier rule based systems where we used to write many rules to solve any specific problem, these days, we need to train our machine learning models with the help of huge corpus (data set). In this paper, we have discussed one of the important applications of AI and computational linguistic i.e. Machine Translation (MT) which translates one natural language to another automatically.MT industry has passed through different phases since its earlier popular approach such as statistical machine translation (SMT) systems and its other version such as phrase based SMT. Performance wise NMT always outperformed SMT on various aspects. However, this holds true only for the languages having large parallel corpora. For low-resource languages, it still remains suboptimal. In this paper, we have applied NMT to low resources Indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it.
{"title":"Experimentation with NMT models on low resource Indic languages","authors":"Nikunj Bansal, Goutam Datta, Ashutosh Kumar Singh","doi":"10.1109/ICIIP53038.2021.9702577","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702577","url":null,"abstract":"Today’s Artificial Intelligence (AI) is data centric. Unlike earlier rule based systems where we used to write many rules to solve any specific problem, these days, we need to train our machine learning models with the help of huge corpus (data set). In this paper, we have discussed one of the important applications of AI and computational linguistic i.e. Machine Translation (MT) which translates one natural language to another automatically.MT industry has passed through different phases since its earlier popular approach such as statistical machine translation (SMT) systems and its other version such as phrase based SMT. Performance wise NMT always outperformed SMT on various aspects. However, this holds true only for the languages having large parallel corpora. For low-resource languages, it still remains suboptimal. In this paper, we have applied NMT to low resources Indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127702041","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702594
Sampath Korra, V. Biksham, Nagunuri Rajender, Tippireddy Chalama Reddy
Building Adaptive Software reusable components are one of the main advantage of Component Based Software Engineering (CBSE). The long-term benefits of an exhaustive domain analysis is that captures the requisites of past, as well as future systems within the domain development of reusable software components and supports the application specific development of the domain. Software developers can use a plug and play approach to facilitate the development and integration of software reusable components. Software reuse procedures and processes should be integrated into the existing software development process, so that software asset library should be created and maintained, so that they will contribute to the design and reuse of software assets. This paper specifies the utilization of software reuse principles, domain engineering techniques, process architecture, process modeling mechanisms and project-categorical processes can be abstracted into reusable components that can be utilized by process engineers.
{"title":"Building Adaptive Software Reusable Components Using Domain Engineering","authors":"Sampath Korra, V. Biksham, Nagunuri Rajender, Tippireddy Chalama Reddy","doi":"10.1109/ICIIP53038.2021.9702594","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702594","url":null,"abstract":"Building Adaptive Software reusable components are one of the main advantage of Component Based Software Engineering (CBSE). The long-term benefits of an exhaustive domain analysis is that captures the requisites of past, as well as future systems within the domain development of reusable software components and supports the application specific development of the domain. Software developers can use a plug and play approach to facilitate the development and integration of software reusable components. Software reuse procedures and processes should be integrated into the existing software development process, so that software asset library should be created and maintained, so that they will contribute to the design and reuse of software assets. This paper specifies the utilization of software reuse principles, domain engineering techniques, process architecture, process modeling mechanisms and project-categorical processes can be abstracted into reusable components that can be utilized by process engineers.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130751946","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}
In the recent era, image tampering has become one of the threatening security problems in digital platforms. There are many software’s available for tampering with an image that depicts as an original image. Different tampering techniques are used to hide important portions from an image or document, one very common practice is a copy-move forgery that is quite impossible to distinguish with an open eye. Authentications of such images are an ardent research area in image processing and computer vision but still a challenging problem. This paper presents a method to identify image tampering that is based on SIFT (Scale Invariant Feature Transform) algorithm. SIFT descriptor is used to extract keypoint features from the input image and a hierarchical clustering algorithm is used to improve the accuracy of identifying the tampered location. The execution time of our proposed method is proportional to image resolution. If one portion of the image is copied and pasted on multiple locations on the same image, our proposed method can identify such occurrences. Finally, Homography is used to show the tampering points and their matching.
{"title":"Identification of Tampering Image Using SIFT Descriptor","authors":"Debjani Chakraborty, Sandeep Choudhury, Sanjib Kumar Dutta, Biswajit Haldar","doi":"10.1109/ICIIP53038.2021.9702601","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702601","url":null,"abstract":"In the recent era, image tampering has become one of the threatening security problems in digital platforms. There are many software’s available for tampering with an image that depicts as an original image. Different tampering techniques are used to hide important portions from an image or document, one very common practice is a copy-move forgery that is quite impossible to distinguish with an open eye. Authentications of such images are an ardent research area in image processing and computer vision but still a challenging problem. This paper presents a method to identify image tampering that is based on SIFT (Scale Invariant Feature Transform) algorithm. SIFT descriptor is used to extract keypoint features from the input image and a hierarchical clustering algorithm is used to improve the accuracy of identifying the tampered location. The execution time of our proposed method is proportional to image resolution. If one portion of the image is copied and pasted on multiple locations on the same image, our proposed method can identify such occurrences. Finally, Homography is used to show the tampering points and their matching.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130770233","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702542
Rohit M. Thanki, Purva Joshi
When any color image transfers through an open-source communication channel, then the security of the color image is compromised against various manipulations. Therefore, the attacker has easily stolen any important color image when transferred from one place to another. For this problem, one of the solutions is digital watermarking, which provides security when data communicate through a channel. But most existing watermarking schemes can hide a small amount of owner identity into a color image to generate a secure watermarked color image. In this paper, a ridgelet transform and singular value decomposition-based watermarking scheme is proposed to tackle this problem, which provides higher payload capacity. Here, the color watermark logo is inserted into singular value of ridgelet coefficients of the color cover image to get the color watermarked image. After that, scrambling-based encryption is applied to it to get an encrypted color watermarked image that provides security before transmission. The experimental results of the proposed scheme show that this new scheme offers better payload capacity and performs better than existing color watermarking schemes.
{"title":"Robust Color Image Watermarking Scheme with High Payload Capacity using FRT - SVD","authors":"Rohit M. Thanki, Purva Joshi","doi":"10.1109/ICIIP53038.2021.9702542","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702542","url":null,"abstract":"When any color image transfers through an open-source communication channel, then the security of the color image is compromised against various manipulations. Therefore, the attacker has easily stolen any important color image when transferred from one place to another. For this problem, one of the solutions is digital watermarking, which provides security when data communicate through a channel. But most existing watermarking schemes can hide a small amount of owner identity into a color image to generate a secure watermarked color image. In this paper, a ridgelet transform and singular value decomposition-based watermarking scheme is proposed to tackle this problem, which provides higher payload capacity. Here, the color watermark logo is inserted into singular value of ridgelet coefficients of the color cover image to get the color watermarked image. After that, scrambling-based encryption is applied to it to get an encrypted color watermarked image that provides security before transmission. The experimental results of the proposed scheme show that this new scheme offers better payload capacity and performs better than existing color watermarking schemes.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134402353","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702679
A. Pal, Debmani Saha
Face detection is a point of interest in many systems. Due to the reason of being less-intrusive characteristic. But various systems are not that much capable of preventing high-level facial spoofing attacks. The attacks are generally done by 3D printed masks or eye-cut photos etc. Detecting micro-expressions in this case can make those systems invulnerable to the attacks because micro-expressions are the only expressions that cannot be controlled. In this article, an approach to detect the micro-expressions of the face has been shown. With the successful detection of micro-expression, the liveness of the face can be detected using a histogram of gradient (HOG) descriptor. This descriptor is used to detect the change in pixel intensities. The descriptor has been applied on specific ROI of the face image i.e., two eyes, nose, and lips of the detected face. The dataset utilized in this project is self-created. The device used to capture the snaps of the dataset is Kinect Xbox One. This approach is effective in preventing such face spoofing attacks.
人脸检测是许多系统感兴趣的一点。由于具有侵入性较小的特点。但是，各种系统都没有那么多的能力来防止高级面部欺骗攻击。攻击通常是通过3D打印面具或眼睛切割照片等来完成的。在这种情况下，检测微表情可以使这些系统免受攻击，因为微表情是唯一无法控制的表情。本文提出了一种检测人脸微表情的方法。在成功检测微表情后，可以使用梯度直方图(HOG)描述符检测人脸的活跃度。这个描述符用于检测像素强度的变化。该描述符被应用于人脸图像的特定ROI，即被检测人脸的两只眼睛、鼻子和嘴唇。本项目使用的数据集是自行创建的。用于捕捉数据集快照的设备是Kinect Xbox One。这种方法可以有效地防止人脸欺骗攻击。
{"title":"Anti-spoofing Performance Enhancement by Facial Micro-expression Detection using Kinect Sensor","authors":"A. Pal, Debmani Saha","doi":"10.1109/ICIIP53038.2021.9702679","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702679","url":null,"abstract":"Face detection is a point of interest in many systems. Due to the reason of being less-intrusive characteristic. But various systems are not that much capable of preventing high-level facial spoofing attacks. The attacks are generally done by 3D printed masks or eye-cut photos etc. Detecting micro-expressions in this case can make those systems invulnerable to the attacks because micro-expressions are the only expressions that cannot be controlled. In this article, an approach to detect the micro-expressions of the face has been shown. With the successful detection of micro-expression, the liveness of the face can be detected using a histogram of gradient (HOG) descriptor. This descriptor is used to detect the change in pixel intensities. The descriptor has been applied on specific ROI of the face image i.e., two eyes, nose, and lips of the detected face. The dataset utilized in this project is self-created. The device used to capture the snaps of the dataset is Kinect Xbox One. This approach is effective in preventing such face spoofing attacks.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134327867","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702692
Ratima Raj Singh, Surbhi Vijh, Diwakar Chaubey
Medical image processing techniques are believed to diagnose the tumor to improve the patient's survival rate. This paper proposed an improved nature-based Tree Growth algorithm (TGA) for selecting the finest features from the feature set derived by the Local Binary Pattern (LBP) and Gray level co-occurrence matrix (GLCM), the images from Brain Magnetic resonance images (MRI) classified as tumor or non-tumor by Random Forest (RF) classifier. The significance of this paper is the creation of an intelligent brain tumor diagnostic system using a Chaotic modified binary tree growth algorithm (CMBTGA-RF). The chaotic map, crossover, and mutation operators are implemented in an updated binary tree growth algorithm for improving exploitation and exploration behaviours. The performance of the proposed methodology is assessed with accuracy, sensitivity, specificity, precision, negative predictive value (NPV), and F-score of 96.42%, 100%, 94.11%, 91.66%, 100%, and 96.96% respectively. The algorithmic findings demonstrate that the CMBTGA-RF with Logistic map works better than the nature-based traditional and recent meta-heuristic algorithms.
{"title":"An Efficient Brain Tumor Detection Using Modified Tree Growth Algorithm and Random Forest Method","authors":"Ratima Raj Singh, Surbhi Vijh, Diwakar Chaubey","doi":"10.1109/ICIIP53038.2021.9702692","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702692","url":null,"abstract":"Medical image processing techniques are believed to diagnose the tumor to improve the patient's survival rate. This paper proposed an improved nature-based Tree Growth algorithm (TGA) for selecting the finest features from the feature set derived by the Local Binary Pattern (LBP) and Gray level co-occurrence matrix (GLCM), the images from Brain Magnetic resonance images (MRI) classified as tumor or non-tumor by Random Forest (RF) classifier. The significance of this paper is the creation of an intelligent brain tumor diagnostic system using a Chaotic modified binary tree growth algorithm (CMBTGA-RF). The chaotic map, crossover, and mutation operators are implemented in an updated binary tree growth algorithm for improving exploitation and exploration behaviours. The performance of the proposed methodology is assessed with accuracy, sensitivity, specificity, precision, negative predictive value (NPV), and F-score of 96.42%, 100%, 94.11%, 91.66%, 100%, and 96.96% respectively. The algorithmic findings demonstrate that the CMBTGA-RF with Logistic map works better than the nature-based traditional and recent meta-heuristic algorithms.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125205934","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}
Pub Date : 2021-11-26DOI: 10.1109/iciip53038.2021.9702589
{"title":"Technical Programme Schedule for ICIIP-2021","authors":"","doi":"10.1109/iciip53038.2021.9702589","DOIUrl":"https://doi.org/10.1109/iciip53038.2021.9702589","url":null,"abstract":"","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134534820","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702705
N. Suhasini, Srilatha Puli
In this paper we will study the two unlike but related technologies – Big Data and cloud computing – and also examines the benefits and outcomes of using cloud computing for Big Data analytics. As information is being produced at a phenomenal scale and it is originating from every direction, such a monstrous measure of information makes enormous or complex informational indexes. These informational collections are known as Big Data. The blend of cloud and big data can be attributed to fresh IT (Information Technology) wave that is causing remarkable progress across IT departments of various industries. In this new environment of big data and cloud there are challenges associated with data storage as data are accumulating rapidly.With rapidly expansion of Big data requires government agencies to extract relevant data and make sense of it in order to make evidence-based policy decisions, with a focus on translating data into information and subsequently information into knowledge. The way to do this is to employ so-called big data analytics, which involves analyzing numerous data sets in order to reveal information such as specific patterns, correlations, and trends, among other things.Big data analytics places rigorous needs on networks, storage, and servers. This is why few businesses use the cloud for this. Big data and cloud are combining to create new business prospects that support big data research while also overcoming numerous architectural challenges.For these mutually exclusive principles to coexist a solution architecture is required to truly exploit. Future breakthroughs and research difficulties in cloud computing that support Big Data analytics are presented in this review.
{"title":"Big Data Analytics in Cloud Computing","authors":"N. Suhasini, Srilatha Puli","doi":"10.1109/ICIIP53038.2021.9702705","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702705","url":null,"abstract":"In this paper we will study the two unlike but related technologies – Big Data and cloud computing – and also examines the benefits and outcomes of using cloud computing for Big Data analytics. As information is being produced at a phenomenal scale and it is originating from every direction, such a monstrous measure of information makes enormous or complex informational indexes. These informational collections are known as Big Data. The blend of cloud and big data can be attributed to fresh IT (Information Technology) wave that is causing remarkable progress across IT departments of various industries. In this new environment of big data and cloud there are challenges associated with data storage as data are accumulating rapidly.With rapidly expansion of Big data requires government agencies to extract relevant data and make sense of it in order to make evidence-based policy decisions, with a focus on translating data into information and subsequently information into knowledge. The way to do this is to employ so-called big data analytics, which involves analyzing numerous data sets in order to reveal information such as specific patterns, correlations, and trends, among other things.Big data analytics places rigorous needs on networks, storage, and servers. This is why few businesses use the cloud for this. Big data and cloud are combining to create new business prospects that support big data research while also overcoming numerous architectural challenges.For these mutually exclusive principles to coexist a solution architecture is required to truly exploit. Future breakthroughs and research difficulties in cloud computing that support Big Data analytics are presented in this review.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307135","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}
Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702645
Nikhlesh Kumar Badoga, Raman Kumar Goyal, R. Mehta
This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.
{"title":"A color image watermarking in the frequency domain using a teaching-learning optimization algorithm","authors":"Nikhlesh Kumar Badoga, Raman Kumar Goyal, R. Mehta","doi":"10.1109/ICIIP53038.2021.9702645","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702645","url":null,"abstract":"This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823167","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}
Cloud computing as a part of artificial intelligence (AI) is the big source for researchers who are focusing on drug designing and development. Cloud computing provides us a platform for not using traditional methods of drug designing which is time-consuming and also requires very high investments of infrastructure, manpower, and chemical requirements. Drug designing software has a pivotal role potential in designing the drug concerning biotechnology and pharmaceutical sciences. All software is well known to be used for analyzing drug molecules, gene expression, and sequence, and 3D structure of proteins and chemical compounds. This review article summarizes the importance of cloud computing in structure-based drug designing and development. This review also emphasizes the top companies providing the software for drug designing and development.
{"title":"Cloud Computing: A relevant Solution for Drug Designing using different Software’s","authors":"Tejinder Kaur, Divya Dhawal Bhandari, Rajiv Sharma","doi":"10.1109/ICIIP53038.2021.9702618","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702618","url":null,"abstract":"Cloud computing as a part of artificial intelligence (AI) is the big source for researchers who are focusing on drug designing and development. Cloud computing provides us a platform for not using traditional methods of drug designing which is time-consuming and also requires very high investments of infrastructure, manpower, and chemical requirements. Drug designing software has a pivotal role potential in designing the drug concerning biotechnology and pharmaceutical sciences. All software is well known to be used for analyzing drug molecules, gene expression, and sequence, and 3D structure of proteins and chemical compounds. This review article summarizes the importance of cloud computing in structure-based drug designing and development. This review also emphasizes the top companies providing the software for drug designing and development.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121115304","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}
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