Published: Jan 31, 2024
Converted to Gold OA:
DOI: 10.4018/IJSI.309960
Volume 12
Open Access
Yogesh M. Kamble, Raj B. Kulkarni
Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very...
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Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very impressive performances. In general, deep architecture models suffer from the over-fitting problem when there is a small number of training data. In this article the attempt is made to remedy this problem in deep architecture with regularization techniques including overlap pooling and flipped image augmentation and dropout; the authors also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. The authors present a hybrid neural-network solution which compares favorably with other methods.
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Kamble, Yogesh M., and Raj B. Kulkarni. "Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning." IJSI vol.12, no.1 2024: pp.1-10. http://doi.org/10.4018/IJSI.309960
APA
Kamble, Y. M. & Kulkarni, R. B. (2024). Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning. International Journal of Software Innovation (IJSI), 12(1), 1-10. http://doi.org/10.4018/IJSI.309960
Chicago
Kamble, Yogesh M., and Raj B. Kulkarni. "Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning," International Journal of Software Innovation (IJSI) 12, no.1: 1-10. http://doi.org/10.4018/IJSI.309960
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Published: Nov 1, 2023
Converted to Gold OA:
DOI: 10.4018/IJSI.333161
Volume 12
Open Access
Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu
Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict...
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Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict valence and arousal values from EEG signals have been established and verified, the EEG data collection process itself is relatively undocumented. This creates an artificial learning curve for new researchers seeking to incorporate EEGs within their research workflow. In this article, a study is presented that illustrates the importance of a strict EEG data collection process for EEG affective computing studies. The work was evaluated by first validating the effectiveness of a machine learning prediction model on the DREAMER dataset, then showcasing the lack of effectiveness of the same machine learning prediction model on cursorily obtained EEG data.
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Estreito, Zachary, et al. "Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition." IJSI vol.12, no.1 2024: pp.1-15. http://doi.org/10.4018/IJSI.333161
APA
Estreito, Z., Le, V., Harris Jr., F. C., & Dascalu, S. M. (2024). Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition. International Journal of Software Innovation (IJSI), 12(1), 1-15. http://doi.org/10.4018/IJSI.333161
Chicago
Estreito, Zachary, et al. "Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition," International Journal of Software Innovation (IJSI) 12, no.1: 1-15. http://doi.org/10.4018/IJSI.333161
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Published: Nov 1, 2023
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DOI: 10.4018/IJSI.333164
Volume 12
Open Access
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu
In many smart city projects, a common choice to capture spatial information is the inclusion of lidar data, but this decision will often invoke severe growing pains within the existing...
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In many smart city projects, a common choice to capture spatial information is the inclusion of lidar data, but this decision will often invoke severe growing pains within the existing infrastructure. In this article, the authors introduce a data pipeline that orchestrates Apache NiFi (NiFi), Apache MiNiFi (MiNiFi), and several other tools as an automated solution to relay and archive lidar data captured by deployed edge devices. The lidar sensors utilized within this workflow are Velodyne Ultra Puck sensors that produce 6-7 GB packet capture (PCAP) files per hour. By both compressing the file after capturing it and compressing the file in real-time; it was discovered that GZIP and XZ both saved considerable file size being from 2-5 GB, 5 minutes in transmission time, and considerable CPU time. To evaluate the capabilities of the system design, the features of this data pipeline were compared against existing third-party services, Globus and RSync.
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Carthen, Chase D., et al. "A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi." IJSI vol.12, no.1 2024: pp.1-14. http://doi.org/10.4018/IJSI.333164
APA
Carthen, C. D., Zaremehrjardi, A., Le, V., Cardillo, C., Strachan, S., Tavakkoli, A., Harris Jr., F. C., & Dascalu, S. M. (2024). A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi. International Journal of Software Innovation (IJSI), 12(1), 1-14. http://doi.org/10.4018/IJSI.333164
Chicago
Carthen, Chase D., et al. "A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi," International Journal of Software Innovation (IJSI) 12, no.1: 1-14. http://doi.org/10.4018/IJSI.333164
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Published: Nov 15, 2023
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DOI: 10.4018/IJSI.333517
Volume 12
Open Access
Partha Ghosh, Takaaki Goto, Leena Jana Ghosh, Giridhar Maji, Soumya Sen
In the countries or areas where the supply-demand ratio of blood is not maintained, the medication process is being deteriorated, and this may be as fatal as death of the patients. It is being...
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In the countries or areas where the supply-demand ratio of blood is not maintained, the medication process is being deteriorated, and this may be as fatal as death of the patients. It is being observed in different areas in different seasons or may be at the time of festival scarcity of blood may happen. On the other hand, if the blood donation camp is organized frequently, there may be a surplus of blood as it has expiry dates. Along with these issues, due to the transportation or mismanagement, blood units are wasted. These problems are addressed in this research work, and methodologies are proposed to determine the most suitable blood bank with respect to the blood donation camp. Further, a demand forecasting algorithm is used both for predicting the blood unit demand of every blood bank and for transferring excess blood units to the blood bank where it is needed the most, and also, for the efficient transportation of the blood units, taxicab geometry-based paths are employed.
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Ghosh, Partha, et al. "A Novel Approach to Organize Blood Donation Camp and Blood Unit Wastage Management." IJSI vol.12, no.1 2024: pp.1-15. http://doi.org/10.4018/IJSI.333517
APA
Ghosh, P., Goto, T., Ghosh, L. J., Maji, G., & Sen, S. (2024). A Novel Approach to Organize Blood Donation Camp and Blood Unit Wastage Management. International Journal of Software Innovation (IJSI), 12(1), 1-15. http://doi.org/10.4018/IJSI.333517
Chicago
Ghosh, Partha, et al. "A Novel Approach to Organize Blood Donation Camp and Blood Unit Wastage Management," International Journal of Software Innovation (IJSI) 12, no.1: 1-15. http://doi.org/10.4018/IJSI.333517
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Published: Dec 15, 2023
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DOI: 10.4018/IJSI.334715
Volume 12
Open Access
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi
Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the...
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Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the standard of their quality education. Thus, the role of the academic result prediction system comes into existence which uses semester grade point average (SGPA) as a metric. The proposed work aims to create a model that can forecast the SGPA of students based on certain traits. It predicts the result in the form of SGPA of computer science students considering their past academic performance, study, and personal habits during their academic semester using different machine learning models, and to compare them based on different accuracy parameters. Some models that are widely used and are found effective in this field are regression algorithms, classification algorithms, and deep learning techniques. The results conclude that deep learning techniques are the most effective in the proposed work because of their high accuracy and performance, depending upon the attributes used in the prediction.
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Bhushan, Megha, et al. "Machine Learning-Based Academic Result Prediction System." IJSI vol.12, no.1 2024: pp.1-14. http://doi.org/10.4018/IJSI.334715
APA
Bhushan, M., Verma, U., Garg, C., & Negi, A. (2024). Machine Learning-Based Academic Result Prediction System. International Journal of Software Innovation (IJSI), 12(1), 1-14. http://doi.org/10.4018/IJSI.334715
Chicago
Bhushan, Megha, et al. "Machine Learning-Based Academic Result Prediction System," International Journal of Software Innovation (IJSI) 12, no.1: 1-14. http://doi.org/10.4018/IJSI.334715
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Published: Feb 27, 2024
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DOI: 10.4018/IJSI.339884
Volume 12
Open Access
Kuo Jong-Yih, Hsieh Ti-Feng, Lin Yu-De, Lin Hui-Chi
Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes....
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Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes. The goal of refactoring is to fix smells in the system. Fixing architectural smells requires more effort than other smells because it is tangled in multiple components in the system. Architecture smells refer to commonly used architectural decisions that negatively impact system quality. They cause high software coupling, create complications when developing new requirements, and are hard to test and reuse. This paper presented a tool to analyze the causes of architectural smells such as cyclic dependency and unstable dependency and included a priority metric that could be used to optimize the smell with the most refactoring efforts and simulate the most cost-effective refactoring path sequence for a developer to follow. Using a real case scenario, a refactoring path was evaluated with real refactoring execution, and the validity of the path was verified.
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Jong-Yih, Kuo, et al. "The Study on Software Architecture Smell Refactoring." IJSI vol.12, no.1 2024: pp.1-17. http://doi.org/10.4018/IJSI.339884
APA
Jong-Yih, K., Ti-Feng, H., Yu-De, L., & Hui-Chi, L. (2024). The Study on Software Architecture Smell Refactoring. International Journal of Software Innovation (IJSI), 12(1), 1-17. http://doi.org/10.4018/IJSI.339884
Chicago
Jong-Yih, Kuo, et al. "The Study on Software Architecture Smell Refactoring," International Journal of Software Innovation (IJSI) 12, no.1: 1-17. http://doi.org/10.4018/IJSI.339884
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Published: May 22, 2024
Converted to Gold OA:
DOI: 10.4018/IJSI.344018
Volume 12
Open Access
Marcello Messina, Ariane de Souza Stolfi, Luzilei Aliel, Ivan Simurra, Damián Keller
A recent initiative within ubimus research contemplates the development of an internet of musical stuff (IoMuSt) as a concept that interacts with and expands the pre-existing rubric of the internet...
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A recent initiative within ubimus research contemplates the development of an internet of musical stuff (IoMuSt) as a concept that interacts with and expands the pre-existing rubric of the internet of musical things (IoMusT). Opposed to the ontological fixedness of things, stuff is pliable, fairly amorphous, changeable depending on usage, context-reliant, either persistent or volatile. It encompasses adaptable and flexible temporalities, featuring non-allotable, non-monetisable and non-reifiable resources. Furthermore, IoMuSt highlights the distinction between object and subject, blurring this crisp separation. The IoMuSt rubric is sustained by aesthetic pliability, fostering an expansion of creative practices and a critical stance towards utilitarian human-computer interaction perspectives. The authors discuss key dimensions of aesthetic pliability as related to flexible infrastructures, open sources and methods, enhanced collaboration and a low ecological footprint. The properties of aesthetic pliability are explored within the realm of two case studies.
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Messina, Marcello, et al. "The Internet of Musical Stuff: Towards an Aesthetically Pliable Musical Internet." IJSI vol.12, no.1 2024: pp.1-19. http://doi.org/10.4018/IJSI.344018
APA
Messina, M., de Souza Stolfi, A., Aliel, L., Simurra, I., & Keller, D. (2024). The Internet of Musical Stuff: Towards an Aesthetically Pliable Musical Internet. International Journal of Software Innovation (IJSI), 12(1), 1-19. http://doi.org/10.4018/IJSI.344018
Chicago
Messina, Marcello, et al. "The Internet of Musical Stuff: Towards an Aesthetically Pliable Musical Internet," International Journal of Software Innovation (IJSI) 12, no.1: 1-19. http://doi.org/10.4018/IJSI.344018
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