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52,205 results on '"recall"'

52. Enhancing Credit Card Default Prediction: Prioritizing Recall Over Accuracy

Author

Onasoga, Bukola, Hwidi, Jamal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, Jaiswal, Ajay, editor, and Kumar, Prabhat, editor

Published
2024
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53. Integrated Gaussian—GLCM Butterfly Optimization with CNN (IGGBOCNN): A Hybrid Approach for Ovarian Cancer Classification in Medical Image Analysis

Author

Wahengbam, Monita, Singh, Tonjam Gunendra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Woungang, Isaac, editor, Dhurandher, Sanjay Kumar, editor, and Singh, Yumnam Jayanta, editor

Published
2024
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54. Predicting Drought Based on Weather and Soil Data Analysis

Author

Gupta, Yogesh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
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55. An Optimal Web Page Re-ranking Based on Improved Rider Optimization

Author

Srinivasa Rao, Pappu, Ramana, T. V., Kallam, Suresh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahapatra, Rajendra Prasad, editor, Peddoju, Sateesh K., editor, Roy, Sudip, editor, and Parwekar, Pritee, editor

Published
2024
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58. Enhancing popSAD: A New Approach to Shilling Attack Detection in Collaborative Recommenders

Author

Shende, Mrunal Kewalram, Verma, Vijay, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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59. ResNet-CPDS: Colonoscopy Polyp Detection and Segmentation Using Modified ResNet101V2

Author

Islam, S. K. Hafizul, Pradhan, Jitesh, Vashistha, Purnendu, Singh, Aman P., Kishore, Aman, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Giri, Debasis, editor, Vaidya, Jaideep, editor, Ponnusamy, S., editor, Lin, Zhiqiang, editor, Joshi, Karuna Pande, editor, and Yegnanarayanan, V., editor

Published
2024
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60. Collaborative Filtering and Content-Based Systems

Author

Kar, Pushpendu, Roy, Monideepa, Datta, Sujoy, Chakrabarti, Amlan, Series Editor, Becker, Jürgen, Editorial Board Member, Hu, Yu-Chen, Editorial Board Member, Chattopadhyay, Anupam, Editorial Board Member, Tribedi, Gaurav, Editorial Board Member, Saha, Sriparna, Editorial Board Member, Goswami, Saptarsi, Editorial Board Member, Kar, Pushpendu, Roy, Monideepa, and Datta, Sujoy

Published
2024
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61. Plant Leaf Disease Detection Using Resnet-50 Based on Deep Learning

Author

Jayashree, S., Sumalatha, V., Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Suresh, N. V., editor, and Buvaneswari, P. S., editor

Published
2024
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63. DRL-CNN Technique for Diabetes Prediction

Author

Usha Nandhini, A., Dharmarajan, K., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Rajagopal, Sridaran, editor, Popat, Kalpesh, editor, Meva, Divyakant, editor, and Bajeja, Sunil, editor

Published
2024
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65. Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage

Author

Foxcroft, Jeremy, Christen, Peter, Antonie, Luiza, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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66. Enhancing Flood Forecasting Accuracy Through Machine Learning Approaches

Author

Shivarudrappa, Halappanavar Ruta, Nandhini, S. P., Pushpa, T. S., Shailaja, K. P., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Sreekeshava, K. S., editor, Kolathayar, Sreevalsa, editor, and Vinod Chandra Menon, N., editor

Published
2024
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67. Analysis Effect of K Values Used in K Fold Cross Validation for Enhancing Performance of Machine Learning Model with Decision Tree

Author

Verma, Vijay Kumar, Saxena, Kanak, Banodha, Umesh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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69. Machine Learning in Finance Case of Credit Scoring

Author

El Maanaoui, Driss, Jeaab, Khalid, Najmi, Hajare, Saoudi, Youness, Falloul, Moulay El Mehdi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Farhaoui, Yousef, editor, Hussain, Amir, editor, Saba, Tanzila, editor, Taherdoost, Hamed, editor, and Verma, Anshul, editor

Published
2024
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71. An Optimized Approach for Sarcasm Detection Using Machine Learning Classifier

Author

Sharma, Surbhi, Joshi, Nisheeth, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Satyasai Jagannath, editor, Yadav, Rajendra Prasad, editor, Gandomi, Amir H., editor, and Saraswat, Mukesh, editor

Published
2024
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73. Monkeypox Disease Classification Using HOG-SVM Model

Author

Kulkarni, Janhavi, Verma, Poorvi, Laddha, Snehal V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Satyasai Jagannath, editor, Yadav, Rajendra Prasad, editor, Gandomi, Amir H., editor, and Saraswat, Mukesh, editor

Published
2024
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74. Analyzing the Performance Variations of Naive Bayes, Linear SVM, and Random Forest for Spam Detection: A Comprehensive Study on the &Quot; Spam or Ham" Dataset

Author

Ojha, Bhawna, Yadav, Pradeep, Arora, Rakhi, Dixit, Nitin, Dubey, Gaurav, Shakyawar, Khemchand, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Devi, B. Rama, editor, Kumar, Kishore, editor, Raju, M., editor, Raju, K. Srujan, editor, and Sellathurai, Mathini, editor

Published
2024
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75. Emotion Recognition: A New Tool for Healthcare Using Deep Learning Algorithms

Author

Mridha, Krishna, Sarker, Tasnim, Zaman, Rawnoquz, Shukla, Madhu, Ghosh, Ankush, Shaw, Rabindra Nath, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shaw, Rabindra Nath, editor, Siano, Pierluigi, editor, Makhilef, Saad, editor, Ghosh, Ankush, editor, and Shimi, S. L., editor

Published
2024
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76. Addressing Vehicle Safety and Platooning Using Low-Cost Object Detection Algorithms

Author

Sharma, Prathmesh, Gangwar, Priti, Gupta, Ritik, Mittal, Poornima, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Mehta, Gayatri, editor, Wickramasinghe, Nilmini, editor, and Kakkar, Deepti, editor

Published
2024
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77. Deep Learning-Based Multi-label Image Classification for Chest X-Rays

Author

Thapa, Maya, Kaur, Ravreet, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Mehta, Gayatri, editor, Wickramasinghe, Nilmini, editor, and Kakkar, Deepti, editor

Published
2024
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78. Speed of Diagnosis for Brain Diseases Using MRI and Convolutional Neural Networks

Author

Srinivasa Rao, B., Gopal, Vankalapati Nanda, Akash, Vatala, Nazeer, Shaik, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Swaroop, Abhishek, editor, Polkowski, Zdzislaw, editor, Correia, Sérgio Duarte, editor, and Virdee, Bal, editor

Published
2024
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79. Analysis and Prediction of Polycystic Ovarian Syndrome Using ML Classifiers

Author

Sanjay, Lavanya, Nayak, Neha, Sriram, Disha, Kodipalli, Ashwini, Rao, Trupthi, Kamal, Shoaib, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shetty, N. R., editor, Prasad, N. H., editor, and Nagaraj, H. C., editor

Published
2024
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81. Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare

Author

Patel, Riyam, Sivaiah, Borra, Patel, Punyaban, Sahoo, Bibhudatta, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Udgata, Siba K., editor, Sethi, Srinivas, editor, and Gao, Xiao-Zhi, editor

Published
2024
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83. Effects of familiar music exposure on deliberate retrieval of remote episodic and semantic memories in healthy aging adults

Author

Bloom, Paul Alexander, Bartlett, Ella, Kathios, Nicholas, Algharazi, Sameah, Siegelman, Matthew, Shen, Fan, Beresford, Lea, DiMaggio-Potter, Michaelle Evangeline, Singh, Anshita, Bennett, Sarah, Natarajan, Nandhini, Lee, Hannah, Sajid, Sumra, Joyce, Erin, Fischman, Rachel, Hutchinson, Samuel, Pan, Sophie, Tottenham, Nim, and Aly, Mariam

Subjects
Biological Psychology, Cognitive and Computational Psychology, Psychology, Applied and Developmental Psychology, Neurosciences, Mental Health, Brain Disorders, Aging, Humans, Adult, Aged, Aged, 80 and over, Healthy Aging, Semantics, Memory, Episodic, Mental Recall, Cues, Familiar music, autobiographical, episodic, recall, semantic, Cognitive Sciences, Experimental Psychology, Applied and developmental psychology, Biological psychology, Cognitive and computational psychology
Abstract

Familiar music facilitates memory retrieval in adults with dementia. However, mechanisms behind this effect, and its generality, are unclear because of a lack of parallel work in healthy aging. Exposure to familiar music enhances spontaneous recall of memories directly cued by the music, but it is unknown whether such effects extend to deliberate recall more generally - e.g., to memories not directly linked to the music being played. It is also unclear whether familiar music boosts recall of specific episodes versus more generalised semantic memories, or whether effects are driven by domain-general mechanisms (e.g., improved mood). In a registered report study, we examined effects of familiar music on deliberate recall in healthy adults ages 65-80 years (N = 75) by presenting familiar music from earlier in life, unfamiliar music, and non-musical audio clips across three sessions. After each clip, we assessed free recall of remote memories for pre-selected events. Contrary to our hypotheses, we found no effects of music exposure on recall of prompted events, though familiar music evoked spontaneous memories most often. These results suggest that effects of familiar music on recall may be limited to memories specifically evoked in response to the music (Preprint and registered report protocol at https://osf.io/kjnwd/).

Published
2023

89. Exploring cognitive biases in pain : investigating attention, interpretation and memory bias

Author

Gaffiero, Daniel

Subjects
Pain, Chronic Pain, Acute Pain, Cognition, Attention, Attentional Bias, Interpretation, Interpretation Bias, Memory, Memory Bias, Recall, Recognition
Abstract

Cognitive-affective models posit that cognitive biases contribute to the aetiology and maintenance of chronic pain. In chronic pain, it is argued that cognitive biases encapsulate interpretation bias, attentional bias, and memory bias. These biases are suggested to exert their influence through the preferential processing of information pertaining to pain, bodily-threat, and harm. Research exploring multiple cognitive biases within the context of a single study is limited. Thus, the role, nature and interaction of these cognitive biases remains poorly understood. This programme of research aimed to address these limitations. Studies 1 and 2 progressed the development and validation of stimulus sets suitable for measuring pain-related attention and interpretation biases in adults. Study 3 then investigated whether a single experience of pain influences cognitive biases in a pain-free sample subjected to acute pain; and study 4 investigated the measurement of cognitive biases, in a chronic pain (vs. non-pain control) sample. Study 1 resulted in the development of two stimulus sets categorised via varying degrees of pain intensity (neutral, low, high) and threat (low, medium, high) to enable rigorous investigation of attentional bias. Study 2 resulted in the development and validation of two ambiguous scenario stimulus sets to enable rigorous investigation of interpretation (and subsequently memory) bias utilising i) forced-choice and ii) free-response paradigms. Supplementary analyses indicated that recent pain experiences positively correlated with the endorsement of pain/pain-illness interpretations of the ambiguous scenarios. Study 3 revealed that a single acute pain experience was not sufficient to influence cognitive biases. However, individuals subjected to a warm water control (as opposed to a cold-pressor task) showed increased attention towards pain-related information, increased recall of pain words immediately following the warm water control, and greater recognition of non-pain words. Additionally, in the acute pain group, measures of pain threshold and tolerance were associated with attention, interpretation, and memory biases. These results indicate a potentially pleasant experience can bias attention toward pain stimulus processing and the importance of pain sensitivity as an influencing cognitive bias factor. Consistent with Study 3, Study 4 provided no evidence of pain-related interpretation or recall biases. However, the chronic pain group exhibited poorer overall recognition performance, compared to their pain-free counterparts. Cross-bias correlations further revealed that as the number of ambiguous scenarios interpreted as pain/pain-illness related increased, so too did the number of pain/pain-illness solutions correctly recalled, irrespective of pain experience. However, correlations between cognitive biases for the non-pain/non-pain illness stimuli were exclusive to the pain-free group. This indicates that the chronic pain group processed scenarios interpreted in a pain/pain-illness manner differently than those they interpreted in a non-pain/non-pain illness manner. Overarching conclusions indicate that individuals with lower pain thresholds and tolerance are more likely to display biased attention, interpretation, and memory favouring pain/pain-illness information; and that individuals with chronic pain display impaired recognition for pain/pain-illness related information. A detailed discussion of these findings is presented in the final chapter, including the proposition of a Pain Sensitivity Model in understanding the role of cognitive biases in pain.

Published
2023
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90. Utilising event cognition principles to explore the effects of location change on memory within immersive virtual environments

Author

Watson, Paul

Subjects
Virtual Reality, Memory, Recall, Interactive Learning Environments
Abstract

The thesis covers a series of studies that explore and investigate the application of event cognition theory to the design of virtual reality (VR) spaces for improved memory recall. The thesis starts with a usability study of a recovery position VR training app developed within the University of Plymouth (Watson 2018, 2021). This work compares the recall and satisfaction of using this application across mobile VR, desktop displays, and pre-recorded video. Based on the first prototype the question then posed is: "Can we better design immersive virtual training spaces that support cognitive processes that might aid learning." Although enhancing memory does not necessarily equate to more learning, recall of information is an important step in many approaches to education and training. Previous real world event cognition work has observed an increase in recall when separating information between rooms, compared to having the same information delivered within a single room (Pettijohn, 2016). This enhancement comes without any specified strategy from the subjects. Segmenting information between VR rooms might also observe similar memory benefits. VR allows complete control over the visual and audio feed to the user, the narrative of events, and affords spatial understanding similar to the real world. Across a further three studies, the thesis explores if recall can be improved by separating information between two immersive virtual rooms: In study 1, using a repeated measures design, subjects are exposed to two word lists within one VR room or between VR two rooms (Watson, 2021). In study 2, using a repeated measures design, subjects are exposed to two word lists within one room or between two rooms. However, both of these conditions are performed in both the real world and the immersive VR world. In study 3, using a repeated measures design, subjects are exposed to two word lists within one room or between two rooms. However, this study also controls for variation of the local and global scene upon word lists delivery. For example whether or not the word lists are delivered with the same voice and aesthetic, or variation within these characteristics. Based on our incremental study design, this work observes no significant benefit for segmenting wordlists between immersive virtual rooms for the population sample presented. To support these studies, a virtual reality tool is developed and refined to teach interaction and navigation paradigms, experimental procedures, and facilitate a realistic cognitive simulation of room environments through believable and distinct aesthetics. Post experiment surveys suggest that the virtual reality tool if fit for purpose as a tutorial tool and to deliver the virtual experimental conditions.

Published
2023

91. Novel Deep Learning Application: Recognizing Inconsistent Characters on Pharmaceutical Packaging [version 2; peer review: 1 approved with reservations, 1 not approved]

Author

Jarmo Koponen, Keijo Haataja, and Pekka Toivanen

Subjects
Research Article, Articles, Text recognition, Machine Vision, Deep Learning, Regions with Convolutional Neural Networks, R-CNN, Character Recognition, Printing Methods, Expiration Date, Batch Codes, Handcrafted Features, Image Recognition, Multi-Object Recognition, OCR, Tesseract OCR, Precision, Recall, Accuracy, F-Measure, Curved Surfaces, Preprocessing
Abstract

Background Machine vision faces significant challenges when applied to text recognition on cardboard packaging particularly due to multiple printing methods, irregular character shapes, and curved packaging surfaces. Methods This research introduces a novel deep learning application for recognizing binarized expiration date and batch code characters printed using multiple printing methods. The method, based on Region-based Convolutional Neural Networks (R-CNN), enables character recognition directly from in the images without the need for extracting handcrafted features. In detail, this approach performs character recognition by using the whole image as input, extracting and learning salient character features directly from the packaging surface images. Results The R-CNN model, with a precision of 91.1% and an F1 score of 80.9%, effectively recognizes manufacturing markings on pharmaceutical packages, with inconsistencies in the characters’ shapes. In a comparative experiment using the same dataset of images, the R-CNN model significantly outperformed Tesseract OCR, achieving much higher precision, recall, and F1 scores. Conclusions The results of this study reveal that the deep learning method outperforms the well-established optical character recognition method in recognizing text characters printed with different printing methods. Presented in this study, the deep learning method recognizes text characters with high precision. It is also suitable for recognizing text printed on curved surfaces, provided proper preprocessing is applied. The problem investigated in the study differs from previous research in the field, focusing on the recognition of texts printed with different printing methods. The research thus fills a gap in text recognition that existed in the research of the field. Furthermore, the study presents new ideas that will be utilized in our future research.

Published
2024
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92. Shared decision-making underutilisation at an outpatient clinic during BMI monitoring in rural Uganda [version 1; peer review: awaiting peer review]

Author

Amon Banturaki, Dickson Kajoba, Theophilus Pius, and Kingsley Akaba

Subjects
Research Article, Articles, Shared-decision-making, body mass index, recall
Abstract

Background Recall of medical instructions is associated with improved adherence to treatment. The recall of the Body Mass Index (BMI) is important for the improvement of adherence to treatments related to obesity. The burden of obesity all over the world has increased, including in many sub-Saharan African countries. This study measured the recall of BMI, shared decision-making (SDM), and patients’ demographics to determine if they were associated. Methods The study was conducted at a rural hospital outpatient clinic in South Western Uganda. Data were collected using a questionnaire and collaboRate-5 tool for measuring shared decision-making. Data were analysed using SPSS version 20. The chi-square test and Pearson coefficient test were done. Results Out of the 92 participants in this study, the median age was 36 years, in an age range of 18 to 87 years, the majority were male 54 (58.7%), most had a background of attending formal education 75 (81.5%), 79 (85.9%) were not able to recall their BMI; 13 (14.1%) were able to recall their BMI. Only gender among demographic factors studied was associated with recall of BMI (p = 0.027). Shared decision-making was associated with the recall of BMI (P < 0.001). Conclusions The proportion of patients who were not able to recall their BMI after the outpatient clinic visit was high. Gender and the use of SDM were associated with recall of BMI. Measured items describing the use of SDM were only reported among 21(22.8%) participants. SDM was underutilized during the study period at the outpatient clinic. There is a need to have continuous medical education on SDM.

Published
2024
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93. Investigations on cardiovascular diseases and predicting using machine learning algorithms

Author

R. P. Ram Kumar, Sanjeeva Polepaka, Vanam Manasa, Deepthi Palakurthy, Errabelli Annapoorna, Navdeep Dhaliwal, Himanshu Dhall, and Laith H. Alzubaidi

Subjects
Heart disease, prediction, recall, precision, classification, UCI repository, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.

Published
2024
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95. Effects of psychopathic traits on preferential recall and recognition of emotionally evocative photos.

Author

Remmel, Rheanna J., Glenn, Andrea L., and Harrison, Alexandra P.

Subjects
*RECOGNITION (Psychology), *PEARSON correlation (Statistics), *MENTAL health, *CRONBACH'S alpha, *MULTIPLE regression analysis, *EMOTIONS, *PHOTOGRAPHY, *PRISONERS, *DESCRIPTIVE statistics, *ANALYSIS of covariance, *ATTENTION, *MEMORY, *PERSONALITY, *NEUROPSYCHOLOGICAL tests, *RESEARCH, *INTERPERSONAL relations, *AUDITORY perception, *VISUAL perception, *DATA analysis software, *COMPARATIVE studies, *PATHOLOGICAL psychology
Abstract

Psychopathic traits are associated with impaired emotional processing. The present study examines the potential association between psychopathic traits and memory for emotional stimuli. Although a significant body of research suggests that memory is heightened for emotional stimuli, it is unclear how psychopathic traits may disrupt this process. Eighty-two male jail inmates completed an emotional memory task as well as portions of a standardised memory assessment. Psychopathic traits were not associated with the ability to freely recall images of positive, negative or neutral valence that participants had seen more than 15 min prior; psychopathic traits were also not associated with the ability to recognise these previously viewed images when shown them again. Exploratory analyses indicated trends toward reduced accuracy in recognising both positive and negative, but not neutral, emotional stimuli in individuals with higher levels of interpersonal and affective traits of psychopathy. As expected, psychopathy was unrelated to non-emotion-related memory functioning in auditory and visual domains as measured by the Wechsler Memory Scales 4th Edition. Overall, these results do not support the hypothesis that psychopathic traits significantly interfere with memory for emotional stimuli. [ABSTRACT FROM AUTHOR]

Published
2024
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96. Patient perspectives on recall period and response options in patient-reported outcomemeasures for chronic rhinosinusitis symptomatology: An international multi-centered study.

Author

Sedaghat, Ahmad R., Derbarsegian, Armo, Yu, Victor T., Alsayed, Ahmed, Bitner, Benjamin F., Yeom, Brian, Liu, David T., Schneider, Sven, Adams, Sarah M., Houssein, Firas A., Walters, Zoe A., Tripathi, Siddhant, Walker, Victoria L., Singerman, Kyle W., Meier, Josh C., Kim, Raymond, Kuan, Edward C., Alsaleh, Saad, and Phillips, Katie M.

Subjects
*PATIENTS' attitudes, *VISUAL analog scale, *SINUSITIS, *SYMPTOM burden, *ENDOSCOPIC surgery, *PATIENT reported outcome measures
Abstract

Background: Existing patient-reported outcome measures (PROMs) for chronic rhinosinusitis (CRS) use a variety of recall periods and response scales to assess CRS symptom burden. Global perspectives of CRS patients regarding optimal recall periods and response scales for CRS PROMs are unknown. Methods: This was a multi-center, cross-sectional study recruiting 461 CRS patients from sites across the United States, Saudi Arabia, New Zealand, and Austria. Participants chose which CRS symptom recall period (1 day, 2 weeks, 1 month, >1 month) was most reflective of their current disease state and upon which to best base treatment recommendations (including surgery). Participants also chose which of six response scales (one visual analogue scale and five Likert scales ranging from four to eight items) was easiest to use, understand, and preferred. Results: A plurality of participants (40.0%) felt their CRS symptoms' current state was best reflected by a 1-month recall period. However, most patients (56.9%) preferred treatment recommendations to be determined by symptoms experienced over a >1 month period. The four- and five-item Likert scales were the easiest to understand (26.0% and 25.4%, respectively) and use (23.4% and 26.7%, respectively). The five-item (26.4% rating it most preferred and 70.9% rating it preferred) and four-item Likert (22.3% rating it most preferred and 56.4% rating it preferred) response scales were most preferred. Conclusion: Future PROMs for CRS should consider assessment of symptoms over a 1-month period and use a four- or five-itemLikert response scale to reflect global patient preferences. These findings also inform interpretation of current CRS PROMs. [ABSTRACT FROM AUTHOR]

Published
2024
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97. Rigorous Assessment of Model Inference Accuracy using Language Cardinality.

Author

Clun, Donato, Shin, Donghwan, Filieri, Antonio, and Bianculli, Domenico

Subjects
FINITE state machines, SYSTEMS software, SOFTWARE engineering
Abstract

Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Nevertheless, models rarely exist in practice and, when they do, get easily outdated; moreover, manually building and maintaining models is costly and error-prone. As a result, a variety of model inference methods that automatically construct models from execution traces have been proposed to address these issues. However, performing a systematic and reliable accuracy assessment of inferred models remains an open problem. Even when a reference model is given, most existing model accuracy assessment methods may return misleading and biased results. This is mainly due to their reliance on statistical estimators over a finite number of randomly generated traces, introducing avoidable uncertainty about the estimation and being sensitive to the parameters of the random trace generative process. This article addresses this problem by developing a systematic approach based on analytic combinatorics that minimizes bias and uncertainty in model accuracy assessment by replacing statistical estimation with deterministic accuracy measures. We experimentally demonstrate the consistency and applicability of our approach by assessing the accuracy of models inferred by state-of-the-art inference tools against reference models from established specification mining benchmarks. [ABSTRACT FROM AUTHOR]

Published
2024
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98. Two cases of driver death caused by airbag rupture.

Author

Miao, Qifeng, Chen, Xinzhe, Lan, Fengchong, Zhao, Xuan, Zhang, Weicheng, Zhang, Meichao, Liu, Dawei, Song, Zhenzhu, Liu, Dongliang, Zhao, Weidong, and Li, Dongri

Subjects
AIR bag restraint systems, PROPELLANTS, AUTOPSY, DEATH rate
Abstract

This article reports two accidents caused by defective Takata airbags ruptured, which led to the deaths of the drivers. This is the first public report on the deaths caused by Takata airbags in China. Determine the relationship between the driver death and airbag rupture through autopsy indings and vehicle inspection. Due to defects in the design of Takata's inflator, moist air was permitted to slowly enter the inflator, resulting the PSAN slowly degraded physically. The damaged propellant burned more rapidly than intended and overpressurized the inflator's steel housing, causing fragmentation and flying debris at high speed, killing or injuring vehicle occupants. To date, there are still tens of millions of defective Takata airbags that have not been recalled for repair, posing safety risks. This article suggests taking preventive measures to avoid the occurrence of similar accidents. [ABSTRACT FROM AUTHOR]

Published
2024
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99. THE VOCAL ADVANTAGE IN MEMORY FOR MELODIES IS BASED ON CONTOUR: EVIDENCE FROM RECALL IN STRING PLAYERS.

Author

WEISS, MICHAEL W. and PERETZ, ISABELLE

Subjects
*STRINGED instrument players, *MELODY, *MEMORY, *VIOLIN playing, *VIOLINISTS
Abstract

RECOGNITION MEMORY IS BETTER FOR VOCAL melodies than instrumental melodies. Here we examine whether this vocal advantage extends to recall. Thirtyone violinists learned four melodies (28 notes, 16 s), two produced by voice and two by violin. Their task was to listen to each melody and then immediately sing (for vocal stimuli) or play back on violin (for violin stimuli) the melody. Recall of the melody was tested in ten consecutive trials. After a brief delay (*10 min), participants were asked to perform the four melodies from memory. Each performance was scored based on the accuracy of two measures: (1) intervals and (2) contour. The results revealed an advantage for vocal over violin melodies in immediate recall of the melodic contour and, after the delay, a reverse pattern with an advantage for violin over vocal melodies. The findings are consistent with the hypothesis that the voice facilitates learning of melodies and further show that the vocal advantage in recall is short-lived and based on contour. [ABSTRACT FROM AUTHOR]

Published
2024
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100. ENHANCED AUTISM SEVERITY PREDICTION: A FUSION OF CONVOLUTIONAL NEURAL NETWORKS AND RANDOM FOREST MODEL.

Author

Ramya, R. and Arokiaraj, S. Panneer

Subjects
CONVOLUTIONAL neural networks, RANDOM forest algorithms, AUTISM spectrum disorders, AUTISM, NEUROLOGICAL disorders, BIOCHEMICAL oxygen demand
Abstract

A neurological condition affecting both the brain and behavior is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the number of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository. [ABSTRACT FROM AUTHOR]

Published
2024
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