Indian Journal of Information Sources and Services最新文献

Exploring the Relationship Between Academic Science and Economics Through Bio-fuel Research: A Scientometric Analysis 通过生物燃料研究探索学术科学与经济学之间的关系：科学计量分析

Indian Journal of Information Sources and Services Pub Date : 2024-03-16 DOI: 10.51983/ijiss-2024.14.1.4112

L. Mohammad Abbas, K. S. Shivraj, U. Perachi Selvi, R. Balasubramani, Seshathiri Dhanasekaran

{"title":"Exploring the Relationship Between Academic Science and Economics Through Bio-fuel Research: A Scientometric Analysis","authors":"L. Mohammad Abbas, K. S. Shivraj, U. Perachi Selvi, R. Balasubramani, Seshathiri Dhanasekaran","doi":"10.51983/ijiss-2024.14.1.4112","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.4112","url":null,"abstract":"Scientometrics is increasingly wielded as a powerful tool in shaping scientific policies, impacting the allocation of funding for projects and institutions by assessing priorities, viewpoints, and capabilities. The investigation of the relationship between academic science and economics is one of the most recent developments in the field of scientific metrics study. It assumes the role of an analysis method of knowledge production and dissemination in innovation systems. These results suggest that biofuel positively affected research collaboration and, as a result, scientific performance. HistCite is one of the study’s tools for analyzing data in a clear and concise manner. Manual searches were conducted on their websites to gather a condensed overview of their data for data mining purposes. Additionally, the Web of Science (WOS) database was utilized for research visualization. Vos Viewer and MS Excel were employed to create graph-like visualizations of data, particularly focusing on the key term 'biofuel' to showcase the field’s research focus and productivity rankings in this area. Statistical techniques and web mining were employed to refine and extract pertinent information. The study period was 33 years (1989-2022), and the results are presented in this paper. This may help to show that the continual growth of plants on our planet greatly outweighs men’s fundamental energy consumption when considering ecological, technical, and economic considerations; only a portion of the biomass that grows can be used to generate energy.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"64 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140236822","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

Exploring the Factors Influencing Usage Behavior of the Digital Library Remote Access (DLRA) Facility in a Private Higher Education Institution in India 探索影响印度私立高等教育机构数字图书馆远程访问（DLRA）设施使用行为的因素

Indian Journal of Information Sources and Services Pub Date : 2024-03-13 DOI: 10.51983/ijiss-2024.14.1.4033

S. Sumithra, Shivam Sakshi

引用次数: 0

Trends and Patterns in Collaborative Authorship: Insights into Advancing Seed Technology Research 合作作者的趋势和模式：推进种子技术研究的启示

Indian Journal of Information Sources and Services Pub Date : 2024-03-13 DOI: 10.51983/ijiss-2024.14.1.4004

T. C. Thirunavukkarasu, S. Thanuskodi, N. Suresh, Ph.D. Research Scholar, Professor Head

引用次数: 0

Unveiling Patterns and Abnormalities of Human Gait: A Comprehensive Study 揭示人类步态的模式和异常：综合研究

Indian Journal of Information Sources and Services Pub Date : 2024-03-13 DOI: 10.51983/ijiss-2024.14.1.3754

Prateek Singhal, Rakesh Kumar Yadav, Upendra Dwivedi

引用次数: 0

A Model for Brain Tumor Detection Using a Modified Convolution Layer ResNet-50 使用修正卷积层的脑肿瘤检测模型 ResNet-50

Indian Journal of Information Sources and Services Pub Date : 2024-02-16 DOI: 10.51983/ijiss-2024.14.1.3753

Rakesh Kumar Yadav, Abhishek Kumar Mishra, Dilip Kumar Jang Bahadur Saini, Hemlata Pant, R. G. Biradar, Pranati Waghodekar

引用次数: 0

Performance Analysis of Five U-Nets on Cervical Cancer Datasets 宫颈癌数据集上五个 U-Net 的性能分析

Indian Journal of Information Sources and Services Pub Date : 2024-02-15 DOI: 10.51983/ijiss-2024.14.1.3916

Priyadarshini Chatterjee, Shadab Siddiqui, Giuseppe Granata, Prasanjit Dey, Razia Sulthana Abdul Kareem

{"title":"Performance Analysis of Five U-Nets on Cervical Cancer Datasets","authors":"Priyadarshini Chatterjee, Shadab Siddiqui, Giuseppe Granata, Prasanjit Dey, Razia Sulthana Abdul Kareem","doi":"10.51983/ijiss-2024.14.1.3916","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.3916","url":null,"abstract":"Image segmentation is crucial for precise analysis and classification of biomedical images, especially in the realm of cervical cancer detection. The accuracy of segmentation significantly influences the efficacy of subsequent image classification processes. While traditional algorithms exist for image segmentation, recent advancements in convolutional neural networks, particularly U-Nets have showcased exceptional effectiveness, especially in the realm of biomedical imaging. This research focuses on evaluating the accuracy of various U-Net architectures applied to three distinct cervical cancer datasets i.e., DSB containing 1340 images, SipakMed containing 1849 images and Intel Images for Screening containing 2000 images datasets taken from 2018 Data Science Bowl. The investigated U-Net architectures comprise the fundamental U-Net, Attention U-Net, Double U-Net, Spatial Attention U-Net, and Residual U-Net. The performance of the u-nets is judged on the metrics: Recall, Precision, F1, Jaccard and Accuracy. It is observed that Basic U-Net on the DSB dataset provides highest value on these metrices and accuracy obtained is 96%. The reason of high accuracy for DSB dataset can be attributed to the contrast of the images which by using co-occurrence matrix is calculated as 145.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774162","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

Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos 在运动视频中使用增强型移动物体检测算法的高效物体检测和分类方法

Indian Journal of Information Sources and Services Pub Date : 2024-02-15 DOI: 10.51983/ijiss-2024.14.1.3895

K. Madhan, N. Shanmugapriya

{"title":"Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos","authors":"K. Madhan, N. Shanmugapriya","doi":"10.51983/ijiss-2024.14.1.3895","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.3895","url":null,"abstract":"Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"198 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834797","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

Performance Analysis of Five U-Nets on Cervical Cancer Datasets 宫颈癌数据集上五个 U-Net 的性能分析

Indian Journal of Information Sources and Services Pub Date : 2024-02-15 DOI: 10.51983/ijiss-2024.14.1.3916

Priyadarshini Chatterjee, Shadab Siddiqui, Giuseppe Granata, Prasanjit Dey, Razia Sulthana Abdul Kareem

{"title":"Performance Analysis of Five U-Nets on Cervical Cancer Datasets","authors":"Priyadarshini Chatterjee, Shadab Siddiqui, Giuseppe Granata, Prasanjit Dey, Razia Sulthana Abdul Kareem","doi":"10.51983/ijiss-2024.14.1.3916","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.3916","url":null,"abstract":"Image segmentation is crucial for precise analysis and classification of biomedical images, especially in the realm of cervical cancer detection. The accuracy of segmentation significantly influences the efficacy of subsequent image classification processes. While traditional algorithms exist for image segmentation, recent advancements in convolutional neural networks, particularly U-Nets have showcased exceptional effectiveness, especially in the realm of biomedical imaging. This research focuses on evaluating the accuracy of various U-Net architectures applied to three distinct cervical cancer datasets i.e., DSB containing 1340 images, SipakMed containing 1849 images and Intel Images for Screening containing 2000 images datasets taken from 2018 Data Science Bowl. The investigated U-Net architectures comprise the fundamental U-Net, Attention U-Net, Double U-Net, Spatial Attention U-Net, and Residual U-Net. The performance of the u-nets is judged on the metrics: Recall, Precision, F1, Jaccard and Accuracy. It is observed that Basic U-Net on the DSB dataset provides highest value on these metrices and accuracy obtained is 96%. The reason of high accuracy for DSB dataset can be attributed to the contrast of the images which by using co-occurrence matrix is calculated as 145.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"605 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139833741","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

Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos 在运动视频中使用增强型移动物体检测算法的高效物体检测和分类方法

Indian Journal of Information Sources and Services Pub Date : 2024-02-15 DOI: 10.51983/ijiss-2024.14.1.3895

K. Madhan, N. Shanmugapriya

{"title":"Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos","authors":"K. Madhan, N. Shanmugapriya","doi":"10.51983/ijiss-2024.14.1.3895","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.3895","url":null,"abstract":"Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"33 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775246","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

Students’ Satisfaction and Learning: Assessment of Teaching-Learning Process in Knowledge Organization 学生的满意度与学习：知识组织中的教学过程评估

Indian Journal of Information Sources and Services Pub Date : 2024-01-20 DOI: 10.51983/ijiss-2024.14.1.3798

Garima Mathur, Navita Nathani, A. Chauhan, S. Kushwah, M. Quttainah

{"title":"Students’ Satisfaction and Learning: Assessment of Teaching-Learning Process in Knowledge Organization","authors":"Garima Mathur, Navita Nathani, A. Chauhan, S. Kushwah, M. Quttainah","doi":"10.51983/ijiss-2024.14.1.3798","DOIUrl":"https://doi.org/10.51983/ijiss-2024.14.1.3798","url":null,"abstract":"This study aims to assess parameters like student perception and student satisfaction towards the interactive teaching-learning process (TLP), which may help teachers at different educational levels to teach more effectively. The teaching-learning process included general regulatory teaching, teacher preparation for learning, regulatory assessment, and student perception and planning for learning. A questionnaire was administered to a sample of classes 11th and 12th with various streams, including Mathematics, Biology, and Commerce as their majors in Central India. This study examined the impact of student perception on their satisfaction with the teaching-learning process (TLP). The results indicated that students’ perception of general regulatory teaching, preparation for learning & regulatory assessment significantly impact their satisfaction towards TLP, but preparation for learning and Regulatory assessment were not associated to satisfaction with teaching. However, it resulted in student learning positively. The findings further indicated that student satisfaction and learning are not different in-stream and class level.","PeriodicalId":447091,"journal":{"name":"Indian Journal of Information Sources and Services","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139523546","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