International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE)
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Volume No 04 Issue No 04 (2020)
No full text[1] Hung, Bui Thanh (2020). Assessment of Recruitment Records using Machine Learning Approach. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 143-151.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.001
[2] Bui,Thien Xuan, Bui,Chuyen Van, Nguyen,Lao, Nguyen,Pha Xuan, Huy, Ha Nguyen Cuong (2020). The Ripening of Pineapple Fruits. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 152-161.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.002
[3] Trong,Nguyen Thanh, Kien,Luong Gia, Tran,Thi T. T., Duong,Hieu N., Hoa,Tran Van, Nam,Thoai (2020). Improving the Performance of One-shot Face Recognition by using Data Augmentation. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 162-170.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.003
[4] Hung,Bui Thanh (2020). Vietnamese Voice Classification based on Deep Learning Approach. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 171-180.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.004
[5] Thai, Dang Nguyen Ha, Quang, Dat Nguyen (2020). Compare model multi-input RNN, LSTM and GRU for prediction of irrigation canal\u27s water level in Red river delta, North Vietnam. International Journal of Machine Learning and Networked Collaborative Engineering, 4(04) pp 181-188.
doi : https://doi.org/10.30991/IJMLNCE.2020v04i04.005  
Comparative Analysis on Machine Learning Algorithms for Multiple Disease Prediction
No full textThese days, majority of the humans are suffered from multiple diseases because of eating habits and environmental situations. Hence, predication of these multiple diseases become a challenging and critical task in these days. Machine Learning (ML) algorithms becomes more popular to predict multiple diseases. For the multiple disease prediction, in this paper, we investigated and examined various ML algorithms such as Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN) used for accurate prediction of disease. For analysis of the ML-based classification algorithms, this paper intently used Accuracy as a performance metric and tested on the DiseaseSymptomKB dataset. The accuracy of general disease prediction by using Decision Tree is 95%, Random Forest is 95%, Naïve Bayes is 95% and KNN is 92%
Compare model multi-input RNN, LSTM and GRUfor prediction of irrigation canal\u27s water level in Red river delta, North Vietnam
No full textForecasting water level on Red River is an important problem in Vietnam. We need to replace water level predicting models that based on experiences of hydro-meteorologists by machine learning models which provide faster as well as more accurate results. Therefore, we have applied several best machine learning methods with arti?cial neural networks such as ANN, RNN, LSTM, and GRU, compared these models. The results indicated that LSTM is most appropriate to Red River data, with 153.5% better than the worst model ANN (in MSE), and 1.58% better than the second best model GRU (in MSE)
Volume No 04 Issue No 03 (2020)
No full textFace Recognition Approach using Stereo Matching Algorithm
Enhancing the Accuracy of Indoor Positioning Using System Delay Time Compensation
E-Recruitment In HR Consultants via E-Technology
A Model on Fuzzy Logic Implementation in the Development of Traffic Management in Smart Cities: Artificial Intelligence Approach
IoT and AI-based plant monitoring syste
Improving the performance of one-shot face recognition by using data augmentation
No full textFor a past few years, the revolution of deep learning techniques has emerged and launched several state-of-the-art models, for instance, the breakthroughs of DeepFace and DeepID to face recognition in 2014. The face recognition in CCTV systems commonly encounters a few obstacles coming from practical conditions, such as ambient light, the diverse positions and angles of cameras, face masks, face poses, and so on. In addition, people who are monitored by the CCTV systems lack photos and typically have only one photo. These problems lead to face recognition reported with unstable performance and difficult to be successfully used in practice. To tackle these problems, this paper proposes an approach, namely ISE, to face augmentation which interpolates multiple samples from an original photo. Particularly, the samples produced by ISE contain real characteristics of cameras in the CCTV systems. By practically deploying a CCTV system at the Bach Khoa Dormitory, ISE indicates that it can boost the performance of face recognition up from 72%, 46% to 84%, 64% in daytime and day-and-nighttime, respectively
Assessment of Recruitment Records using Machine Learning
No full textIn the era of the fourth industrial revolution (Industry 4.0), the applications of Information Technology (IT) have been widely used in various aspects of life. As the result, analyzing and predicting the result for the application of candidates as well as employers are also growing significantly. Jobseekers and employers want to have accurate information and prediction results in order to have suitable job proposals for themselves and candidates. This research is conducted based on using Machine Learning to meet the requirements of jobseekers and employers in the recruitment evaluation process. We propose to use 4 machine learning methods – Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Recurrent Neural Network (RNN) to predict job applications. The data set is collected from the Job Center of Binh Duong province. On the basis of the best results method, we build a job application review and visualize the results
Vietnamese Voice Classification based on Deep Learning Approach
No full textIn the digital era, it is undeniable that voice classification plays a meaningful task in various aspects of life. In this research, we propose a method of predicting the gender and region of the Vietnamese voice which is based on the spectrum of sound using the deep learning approach. From the raw dataset, we conducted the preprocessing stage to take the audio dataset to the same frequency and time standard. After that, we extracted Mel Spectrogram feature and then put into a deep learning model - Convolutional Neural Network to train and optimize. Our experiments on 37 samples taken from VIVOS corpus audio dataset achieve the accuracy of 86.48% for predicting gender and 51.45% for predicting the region of the voic
RFID Security and Human Microchipping Privacy and Concerns
No full textRadio Frequency Identification (RFID) technology is used to identify items remotely. The RFID system consists of three main parts: an RFID tag, which contains data about an item; an RFID reader; and an antenna that transmits radio signals between the tag and the reader. This system has many applications to identify and track objects and people — human microchipping. Therefore, besides the security threats associated with RFID systems, when technology is related to people, privacy will be at more risk. In this paper, some RFID security and privacy concerns will be addressed, along with corresponding countermeasures. Human microchipping will be discussed along with available legislation in the United States
Automated Self-screening System Using Chat Bot
No full textThis paper depicts the creation of an automated screening system by using a Microsoft health care bot. The system will have a web interface where the user will take the screening by choosing the applicable option as an answer. The system then analyses the answers provided by the user and performs one of the following actions based on the result of the screening.
Allow the user to come to the office and send an email.
Asks the user to stay in the quarantine for 14 days and send the same in email.
Keeps the user in the waiting state if the user doesn’t know the covid test results
A Model on Fuzzy Logic Implementation in the Development of Traffic Management in Smart Cities: Artificial Intelligence Approach
No full textSmart cities have been developing using a combination of new emerging technologies which majorly Include Internet of Things (IoT), Big Data, Blockchain, Augmented Reality, and Artificial Intelligence (AI). These technologies have contributed to the development of many fields such as transportation, environmental protection, energy, medical care, and logistics, and have produced many social, economic and ecological benefits Traffic monitoring has become one of the censorious issues in large cities. The Latest traffic light frameworks utilize a fixed time delay for various traffic directions and do follow a specific cycle while changing starting with one sign then onto the next. This makes undesirable blockage during top hours, loss of worker hours, and in the long run decreases profitability. A smart city is one that extraordinarily decreases vehicle traffic and permits individuals and merchandise to be moved without any problem. Wise traffic frameworks are a case of this and the accomplishment of independent vehicle transportation would be a prime case of achievement for a smart city, as this could decrease vehicle-related passing’s. Every one of these endeavors would lessen contamination, bringing about a more beneficial populace. We propose to you a system that has proven to be smart, intelligent, and capable of solving traffic system issues that offer to be a boon to smart cities and control systems emerging cities of the worl