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International Journal of Software Innovation
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ISSN (Print) 2166-7160 - ISSN (Online) 2166-7179
Published by IGI Global

[146 journals]

A Novel Approach to Organize Blood Donation Camp and Blood Unit Wastage
Management
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  Authors: IGI Global
  Abstract:
  Authors : Partha Ghosh (Academy of Technology, Naihati, India), Takaaki Goto (Toyo University, Japan), Leena Jana Ghosh (J.C. Edutech, India), Giridhar Maji (Asansol Polytechnic, India), Soumya Sen (University of Calcutta, India)
  Volume/Issue: 12/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.333517
  Date Posted: 11/15/2023 12:00:00 AMIn 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.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Wed, 15 Nov 2023 00:00:00 GMT
Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition
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  Authors: IGI Global
  Abstract:
  Authors : Zachary Estreito (University of Nevada, Reno, USA), Vinh Le (University of Nevada, Reno, USA), Frederick C. Harris Jr. (University of Nevada, Reno, USA), Sergiu M. Dascalu (University of Nevada, Reno, USA)
  Volume/Issue: 12/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.333161
  Date Posted: 11/1/2023 12:00:00 AMPredicting 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.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Wed, 01 Nov 2023 00:00:00 GMT
A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi
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  Authors: IGI Global
  Abstract:
  Authors : Chase D. Carthen (University of Nevada, Reno, USA), Araam Zaremehrjardi (University of Nevada, Reno, USA), Vinh Le (University of Nevada, Reno, USA), Carlos Cardillo (University of Nevada, Reno, USA), Scotty Strachan (Nevada System of Higher Education, USA), Alireza Tavakkoli (University of Nevada, Reno, USA), Frederick C. Harris Jr. (University of Nevada, Reno, USA), Sergiu M. Dascalu (University of Nevada, Reno, USA)
  Volume/Issue: 12/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.333164
  Date Posted: 11/1/2023 12:00:00 AMIn 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.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Wed, 01 Nov 2023 00:00:00 GMT
A Two-Stage Long Text Summarization Method Based on Discourse Structure
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  Authors: IGI Global
  Abstract:
  Authors : Xin Zhang (Communication University of China, China), Qiyi Wei (Communication University of China, China), Qing Song (Communication University of China, China), Pengzhou Zhang (Communication University of China, China)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.331091
  Date Posted: 9/29/2023 12:00:00 AMThis paper proposes a two-stage automatic text summarization method based on discourse structure, aiming to improve the accuracy and coherence of the summary. In the extractive stage, a text encoder divides the long text into elementary discourse units (EDUs). Then a parse tree based on rhetorical structure theory is constructed for the whole discourse while annotating nuclearity information. The nuclearity terminal nodes are selected based on the summary length requirement, and the key EDU sequence is output. The authors use a pointer generator network and a coverage mechanism in the generation stage. The nuclearity information of EDUs is to update the word attention distribution in the pointer generator, which not only accurately reproduces the critical details of the text but also avoids self-repetition. Experiments on the standard text summarization dataset (CNN/DailyMail) show that the ROUGE score of the proposed two-stage model is better than that of the current best baseline model, and the summary achieves corresponding improvements in accuracy and coherence.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 29 Sep 2023 00:00:00 GMT
Sentiment Analysis of Hybrid Network Model Based on Attention
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  Authors: IGI Global
  Abstract:
  Authors : Hongzhan Zhen (Communication University of China, China), Wenqian Shang (Communication University of China, China), Wanyu Zhang (Communication University of China, China)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.327364
  Date Posted: 8/4/2023 12:00:00 AMThe existing text sentiment analysis models based on deep learning and neural network usually have problems such as incomplete text feature extraction and failure to consider the impact of key information on text sentiment tendency. Based on the parallel hybrid network and the two-way attention mechanism, an improved text sentiment analysis model is proposed. The model first takes the word vector trained by the BERT language model as the input, and then extracts the global and local features of the context simultaneously through the parallel hybrid neural network constructed by the Convolution Neural Network (CNN) and The Bidirectional Gated Recurrent Unit (BiGRU), so as to improve the feature extraction ability of the model. It also integrates the dual-way attention mechanism to strengthen the key information in the global feature and local feature, and the feature vectors obtained by feature fusion are used for sentiment analysis.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 04 Aug 2023 00:00:00 GMT
Vehicle Type Classification Using Hybrid Features and a Deep Neural
Network
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  Authors: IGI Global
  Abstract:
  Authors : Sathyanarayana N. (Vemana Institute of Technology, India), Anand M. Narasimhamurthy (International School of Engineering, Bengaluru, India)
  Volume/Issue: 10/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.297511
  Date Posted: 3/31/2023 12:00:00 AMIn this research, a framework incorporating hybrid features is proposed to improve the performance of vehicle type classification. The proposed model includes a camera response model to enhance the collected images and a Gaussian mixture model to localize the object of interest. The feature vectors are extracted from the pre-processed images using Gabor features, histogram of oriented gradients, and local optimal-oriented pattern. The hybrid set of features discriminate the classes better; further, an ant colony optimizer is used to reduce the dimension of the extracted feature vectors. Finally, deep neural network is used to classify the types of vehicles in the images. The proposed model was tested on the MIO vision traffic camera dataset and a real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures like AlexNet, Inception V3, ResNet 50, VGG 19, Xception, and DenseNet.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 31 Mar 2023 00:00:00 GMT
Road Rage and Aggressive Driving Behaviour Detection in Usage-Based
Insurance Using Machine Learning
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  Authors: IGI Global
  Abstract:
  Authors : Subramanian Arumugam (Vellore Institute of Technology, Chennai, India), R. Bhargavi (Vellore Institute of Technology, Chennai, India)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.319314
  Date Posted: 3/2/2023 12:00:00 AMDriving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Thu, 02 Mar 2023 00:00:00 GMT
Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional
Convolutional Neural Network
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  Authors: IGI Global
  Abstract:
  Authors : Moses A. Agana (University of Calabar, Nigeria), Chukwuemeka Odi Agwu (Ebonyi State University, Abakaliki, Nigeria), Nsinem A. Ukpoho (University of Calabar, Nigeria)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.316169
  Date Posted: 1/20/2023 12:00:00 AMBreast cancer has a devastating effect on women. Different strategies of breast cancer classification exist with minimal work done on the prediction of the occurrence of the disease in potential carriers. In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural network (CNN) was used for breast cancer classification. Histopathological images were used for cancer prediction. Python was used as the programming language for implementing the system. The model was tested using datasets from The Cancer Imaging Archive (TCIA) repository. An accuracy level of 98.8% (higher than the most recent existing model) was achieved for the prediction of the future occurrence of breast cancer based on the tests on the dataset. The application of the model using live data from women can help in the prediction and control of the occurrence of breast cancer amongst women.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 20 Jan 2023 00:00:00 GMT
Effective Classification of Chronic Kidney Disease Using Extreme Gradient
Boosting Algorithm
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  Authors: IGI Global
  Abstract:
  Authors : Ramya Asalatha Busi (Vasireddy Venkatadri Institute of Technology, India), M. James Stephen (Wellfare Institute of Science Technology & Management, India.)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.315732
  Date Posted: 1/13/2023 12:00:00 AMWith a high rate of morbidity and mortality, chronic kidney disease is a global health issue that also causes other diseases. Patients frequently overlook the condition because there aren't any evident symptoms in the early stages of CKD. An efficient and effective Extreme gradient boosting method for the early diagnosis of kidney illness has been proposed in this paper to explore the capability of various machine learning algorithms. DenseNet can extract a variety of features such as vector features. After that feature extraction phase, the data are fed into the feature selection phase. The features are selected based upon the Improved Salp swarm Algorithm (ISSA). The proposed CKD classification method has been simulated in PYTHON. Utilizing the CKD dataset from the UCI machine learning resources, the dataset is then tested. Sensitivity, accuracy, and specificity are the performance metrics used for the proposed CKD classification approach. The results of the experiments demonstrate that the proposed approach outperforms the present state-of-the-art method in classifying CKD.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 13 Jan 2023 00:00:00 GMT
An Outlook Architecture: Protocols and Challenges in IoT and Future Trends
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  Authors: IGI Global
  Abstract:
  Authors : Kajal Patel (Gujarat Technological University, India), Mihir Mehta (Gujarat Technological University, India)
  Volume/Issue: 11/1
  ISSN: 2166-7160
  EISSN: 2166-7179
  
  DOI : 10.4018/IJSI.315744
  Date Posted: 1/6/2023 12:00:00 AMThe internet of things (IoT) has recently received much attention due to its revolutionary potential. The internet of things facilitates data interchange in a large number of possible applications, including smart transportation, smart health, smart buildings, and so on. As a result, these application domains can be grouped to form smart life. In response to the IoT's rapid growth, cybercriminals and security professionals are racing to keep up. Billions of connected devices can exchange sensitive information with each other. As a result, securing IoT and protecting users' privacy is a huge concern. A session for communication in a network is established by authenticating and validating the device's identity and checking whether it is a legal device. The IoT technology can be used for various applications only if challenges related to IoT security can be overcome.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Software Innovation (IJSI) click here.
  PubDate: Fri, 06 Jan 2023 00:00:00 GMT