| 1. |
- Arfizurrahmanl, Mohammad, et al.
(författare)
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Real-time non-intrusive driver fatigue detection system using belief rule-based expert system
- 2021
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Ingår i: Journal of Internet Services and Information Security (JISIS). - : Innovative Information Science and Technology Research Group. - 2182-2069 .- 2182-2077. ; 11:4, s. 44-60
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a non-intrusive system for detecting driver fatigue in real-time. To determine the level of fatigue the system uses various visual features, namely head nodding, eye closure duration and yawning. A state-of-the-art facial landmark detector ’IntraFace’ has been adopted to determine the eye state, mouth state and head pose estimation. However, different forms of uncertainties such as vagueness, imprecision, ambiguity and incompleteness are involved in calculating these visual parameters. Therefore, a Belief Rule-Based Expert System (BRBES) is employed, which has the ability to handle the uncertainties. The information of the visual parameters is sent to BRBES as input to determine the level of fatigue. An optimal learning model has been developed to improve the performance and accuracy of the BRBES. A comparison between the system and the fuzzy rulebased expert system has been carried out. The system generates more effective and reliable results than the fuzzy rule-based expert system.
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| 2. |
- Dey, Polash, et al.
(författare)
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Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains
- 2021
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Ingår i: Algorithms. - Basel, Switzerland : MDPI. - 1999-4893. ; 14:8, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.
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| 3. |
- Gupta, Dipankar, et al.
(författare)
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A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection
- 2019
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Ingår i: 2019 IEEE International Conference on Robotics, Automation, Artificial- Intelligence and Internet-of-Things. - : IEEE. ; , s. 116-121
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Konferensbidrag (refereegranskat)abstract
- Though speech recognition has been a common interest of researchers over the last couple of decades, but very few works have been done on Bangla voice recognition. In this research, we developed a digital personal assistant for handicapped people which recognizes continuous Bangla voice commands. We employed the cross-correlation technique which compares the energy of Bangla voice commands with prerecorded reference signals. After recognizing a Bangla command, it executes a task specified by that command. Mouse cursor can also be controlled using the facial movement of a user. We validated our model in three different environments (noisy, moderate and noiseless) so that the model can act naturally. We also compared our proposed model with a combined model of MFCC & DTW, and another model which combines crosscorrelation with LPC. Results indicate that the proposed model achieves a huge accuracy and smaller response time comparing to the other two techniques.
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| 4. |
- Gupta, Dipankar, et al.
(författare)
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An Interactive Computer System with Gesture-Based Mouse and Keyboard
- 2021
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Ingår i: Intelligent Computing and Optimization. - Cham : Springer Nature. ; , s. 894-906
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Konferensbidrag (refereegranskat)abstract
- Researchers around the world are now focused on to make our devices more interactive and trying to make the devices operational with minimal physical contact. In this research, we propose an interactive computer system which can operate without any physical keyboard and mouse. This system can be beneficial to everyone, especially to the paralyzed people who face difficulties to operate physical keyboard and mouse. We used computer vision so that user can type on virtual keyboard using a yellow-colored cap on his fingertip, and can also navigate to mouse controlling system. Once the user is in mouse controlling mode, user can perform all the mouse operations only by showing different number of fingers. We validated both module of our system by a 52 years old paralyzed person and achieved around 80% accuracy on average.
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| 5. |
- Hawlader, Mohammad Delwer Hossain, et al.
(författare)
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The art of forming habits : applying habit theory in changing physical activity behaviour
- 2023
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Ingår i: Journal of Public Health. - : Springer Nature. - 2198-1833 .- 1613-2238. ; 31:12, s. 2045-2057
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Tidskriftsartikel (refereegranskat)abstract
- Background Habits are obtained as a consequence of cue-contingent behavioural repetition. Context cues stimulate strong habits without an individual contemplating that action has been initiated. Because of its health-enhancing effects, making physical activity a part of one's life is essential. This study examined the associations of physical activity (PA) behaviours with PA habits and the role of autonomous motivation in developing PA habits. Methods This study used a cross-sectional design. A structured questionnaire was implemented through emails to 226 university students, where PA levels, habits and autonomous motivation were self-reported. Results Binary logistic regression identified age groups, gender and participants who were trying to lose weight as the significant predictors in meeting physical activity guidelines. Path analysis showed that moderate-intensity physical activity (beta = 0.045, CI = 0.069-0.248) and strength training exercises (beta = 0.133, CI = 0.148-0.674) were significantly associated with PA habits (p < 0.01). Autonomous motivation was directly associated with PA habits (beta = 0.062, CI = [0.295-0.541], p < 0.01) and was also significantly related to moderate-intensity physical activity (beta = 0.243, CI = [0.078-0.266], p < 0.01) and strength training exercises (beta = 0.202, CI = [0.033-0.594], p < 0.05). Conclusions The emphasis on experiment-based logic and interest in habit formation in the research community is extensive. As the college years offer an excellent opportunity to establish healthy behavioural interventions, encouraging students in regular PA and exhibiting an autonomous motivation towards PA may be necessary.
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| 6. |
- Morshed, Muhammad Sarwar Jahan, et al.
(författare)
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Integration of wireless hand-held devices with the cloud architecture : Security and privacy issues
- 2011
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Ingår i: Proc. - Int. Conf. P2P, Parallel, Grid, Cloud Internet Comput., 3PGCIC. - 9780769545318 ; , s. 83-88
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Konferensbidrag (refereegranskat)abstract
- Use of wireless hand held devices like mobile, PDA, laptop etc. is increasing rapidly. Many advanced users want more functionality with these wireless devices to manage their daily schedule. But most of the wireless hand-held devices have limited resource capability for robust functionality. Therefore cloud computing environment could be an alternative solution for these devices to support resource consuming applications. If the wireless hand held device is connected with the cloud, user can use more resource consuming applications and private data (stored in the cloud) from those devices. But privacy and security of the personal information and data make the user concern for using the cloud. The aim of the paper is to identify the security and privacy related risks and threats of the mass users as well as corporate users, if the wireless hand-held devices will be integrated with the cloud.
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| 7. |
- Tarek, Iftakher Hasan Mohammad, et al.
(författare)
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A Hybrid Hotel Recommendation Using Collaborative, Content Based and Knowledge Based Approach
- 2023. - 1
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Ingår i: Intelligent Computing & Optimization. - Cham : Springer. ; , s. 1049-1057
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Bokkapitel (refereegranskat)abstract
- Everybody plans vacations, and the first step in that process is to book a hotel. With the hospitality sector being so competitive, it’s critical to maintain best practices and stay on top of client demands and wants. They want individualized experiences, one-of-a-kind amenities, and a general sense of well-being on all levels. A consumer of a hotel recommendation system frequently encounters challenges in obtaining and fulfilling his or her wishes. Content-based filtering and collaborative filtering are two well-known strategies for creating a recommender system. Content-based filtering does not use human opinions to produce predictions, whereas collaborative filtering does, resulting in more accurate predictions. Collaborative filtering, on the other hand, cannot forecast objects that have never been rated by anyone. Both approaches can be merged with a hybrid methodology to cover the disadvantages of each approach while gaining the benefits of the other. This research employed Item-Item collaborative filtering (CF) and content-based filtering (CB) to calculate hotel similarity in our suggested method. It uses cosine similarity to calculate user similarity. For content-based filtering, natural language processing (NLP) is also employed. Our model employs a knowledge-based approach for Cold-User scenarios. Precision, recall and f1 used to evaluate the recommendation system.
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| 8. |
- Uddin Ahmed, Tawsin, et al.
(författare)
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An Integrated Real-Time Deep Learning and Belief Rule Base Intelligent System to Assess Facial Expression Under Uncertainty
- 2020
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Ingår i: 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Nowadays, the recognition of facial expression draws significant attention in various domains. In view of this, a realtime facial expression recognition system has been developed using a Deep Learning approach, which can classify ten emotions, including angry, disgust, fear, happy, mockery, neutral, sad, surprise, think, and wink. In addition, an integrated expert system has also been developed by integrating Deep Learning with a Belief Rule Base to support the assessment of the overall mental state of a person over a period of time from video streaming data under uncertainty. In this research, data-driven and knowledge-driven approaches are integrated together to assess the mental state of an individual. Such a system could enable the identification of a suspect before committing any crime beforehand by the law enforcement agency. The performance of this integrated system is found reliable than existing methods of facial expression assessment. Contribution- The paper presents a noble method of computing the overall mental condition of a person by integrating CNN and BRBES under uncertainty. Contribution- The paper presents a noble method of computing the overall mental condition of a person by integrating CNN and BRBES under uncertainty.
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| 9. |
- Afroze, Tasnim, et al.
(författare)
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Glaucoma Detection Using Inception Convolutional Neural Network V3
- 2021
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Ingår i: Applied Intelligence and Informatics. - Cham : Springer. ; , s. 17-28
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Konferensbidrag (refereegranskat)abstract
- Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.
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| 10. |
- Ahmed, Faisal, et al.
(författare)
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Comparative Performance of Tree Based Machine Learning Classifiers in Product Backorder Prediction
- 2023. - 1
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Ingår i: Intelligent Computing & Optimization. - Cham : Springer. ; , s. 572-584
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Bokkapitel (refereegranskat)abstract
- Early prediction of whether a product will go to backorder or not is necessary for optimal management of inventory that can reduce the losses in sales, establish a good relationship between the supplier and customer and maximize the revenues. In this study, we have investigated the performance and effectiveness of tree based machine learning algorithms to predict the backorder of a product. The research methodology consists of preprocessing of data, feature selection using statistical hypothesis test, imbalanced learning using the random undersampling method and performance evaluating and comparing of four tree based machine learning algorithms including decision tree, random forest, adaptive boosting and gradient boosting in terms of accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve and area under the precision and recall curve. Three main findings of this study are (1) random forest model without feature selection and with random undersampling method achieved the highest performance in terms of all performance measure metrics, (2) feature selection cannot contribute to the performance enhancement of the tree based classifiers, and (3) random undersampling method significantly improves performance of tree based classifiers in product backorder prediction.
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