2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)最新文献_第2页

Artificial Intelligence on Single Board Computers: An Experiment on Sound Event Classification 单板计算机上的人工智能:声音事件分类实验

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664746

Sulakna Karunaratna, Pasan Maduranga

引用次数: 3

Personalized Travel Recommendation System Using an Ontology 基于本体的个性化旅游推荐系统

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664718

Hansika Gunasekara, Thushari P. Silva

引用次数: 1

Mind Relaxation Chatbot for University Students by Using Dense Neural Network 基于密集神经网络的大学生心灵放松聊天机器人

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664678

Heshani Bopage, Chinthanie Weerakoon

{"title":"Mind Relaxation Chatbot for University Students by Using Dense Neural Network","authors":"Heshani Bopage, Chinthanie Weerakoon","doi":"10.1109/SLAAI-ICAI54477.2021.9664678","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664678","url":null,"abstract":"Relaxation is the emotional state of a living being, of low tension, in which there is an absence of arousal that could come from sources such as anger, anxiety, or fear. Technology can be used to mind relaxation. Chatbot is an automated computer software program capable of having intelligent live conversations with people. It is a technology that provides a new way to interact with computer systems. Nowadays, chatbots are very popular in a large scale of applications, especially in systems that provide intelligence support to the user. It is one of the technologies that has been used successfully in many fields such as education, marketing and health field. This paper aims to develop such a chatbot for the mind relaxation of university students using Natural Language processing techniques and Dense Neural Network. The chatbot tool was trained with a series of counseling conversations in text form. Training phases included intents, tags, patterns and responses. Primary function of chatbot have to play is to understand the intents of students and to respond to them appropriately. Input and Output are in the form of the text.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Architectures used in Artificial Cognitive Systems for Embodiment 用于体现的人工认知系统的架构

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664660

Madhuka D. Bandara, Sandeepa Viduranga, Nipun Rodrigo, Menaka Ranasinghe

{"title":"Architectures used in Artificial Cognitive Systems for Embodiment","authors":"Madhuka D. Bandara, Sandeepa Viduranga, Nipun Rodrigo, Menaka Ranasinghe","doi":"10.1109/SLAAI-ICAI54477.2021.9664660","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664660","url":null,"abstract":"Systems integrated with the power of artificial Intelligence and embodied in real environments are having the power of operating autonomously and is capable of performing real-time activities with emotional intelligence. Such systems are simply known as artificial cognitive systems embodied in real environments. These systems are capable of taking sensory inputs process them and act in the environment with cognitive capabilities. The cognitive capabilities of these systems are totally dependent on the architecture adopted in these systems. These cognitive architectures provide an abstract perception model for these cognitive systems. These cognitive architectures are inspired by how physical systems operates autonomously by the combination of brain, mind and body combination thus obligating the necessity of investigating system embodiment considering physical computational properties as well as non-representational properties. Hence this paper investigates the existing architectures for embodiment. It is noted that achieving complete embodiment of systems on par with humans poses a research challenge and is being extensively investigated. Through the literature review it is identified that the use of emergent architectures combined with hybrid approaches as a feasible way of achieving embodiment in artificial cognitive systems.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124050867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Agile Software Development Life Cycle Model for Machine Learning Application Development 面向机器学习应用开发的敏捷软件开发生命周期模型

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664736

R. Ranawana, A. Karunananda

引用次数: 7

Hybrid Filter-Wrapper Approach for Feature Selection in Deceptive Consumer Review Classification 欺骗性消费者评论分类中特征选择的混合滤波-包装方法

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664748

D. Vidanagama, Thushari P. Silva, A. Karunananda

{"title":"Hybrid Filter-Wrapper Approach for Feature Selection in Deceptive Consumer Review Classification","authors":"D. Vidanagama, Thushari P. Silva, A. Karunananda","doi":"10.1109/SLAAI-ICAI54477.2021.9664748","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664748","url":null,"abstract":"Nowadays, due to the prevailing situation of the world, people are heavily focusing on online transactions. There has been a rapid increase in online transactions and several types of data generated through such transactions during the last few years. As there is no other involvement in purchasing decisions, customers make purchasing judgments through the reviews. Therefore, not only for making purchasing decisions but also customer reviews provide valuable information regarding the products for decision-makers. By considering this as an advantage, fraudulent reviewers tend to write reviews to promote or downgrade products. Deceptive reviews can be identified via reviewer behavioural features, content-related features, or review features. But all the extracted features may not be critical for identifying deceptive. This research introduces a novel filter-wrapper hybrid approach to select optimal features to identify deceptive online customer reviews. A combination of univariate and multivariate filter methods as well as a wrapper method with the bidirectional search were used to select the features. The model was evaluated using the K-Nearest Neighbor (KNN) classifier. The proposed hybrid approach shows the highest model accuracy against the sole traditional approaches. The selected optimal features used for model building are effective as they reveal the most statistically significant features when predicting the deceptive reviews..","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121091804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recommender System based on Food and Exercise Ontologies to Find the Suitable Fitness Exercise Plan with the Aid of Python 基于食物和运动本体的推荐系统在Python的帮助下寻找合适的健身运动计划

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664742

Chamali Basnayake, C. Peiris, H. Wickramarathna, Poornima Jayathunga

{"title":"Recommender System based on Food and Exercise Ontologies to Find the Suitable Fitness Exercise Plan with the Aid of Python","authors":"Chamali Basnayake, C. Peiris, H. Wickramarathna, Poornima Jayathunga","doi":"10.1109/SLAAI-ICAI54477.2021.9664742","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664742","url":null,"abstract":"In the modern world, professionals of diverse industrial sectors have severely become victims of Obese and Overweight conditions. Obese and Overweight conditions can be minimized by having proper dietary plans, physical activities and minimizing alcohol-based relaxation. In this research context, we try to address the issue of having poor physical exercise. We guide professionals with suitable exercises to reduce their weight in order to have the required Body Mass Index(BMI). The user body measurements that are recommended by the domain experts to concern such as sex, height, weight, exercise preferences, age, diet details and medical history used to calculate the degree of obesity of each individual. Then the degree of obesity is mapped with the knowledge base along with the predefined rules in order to match respective exercises suitable for the particular individuals that are compatible with the user’s medical history. Two ontologies for foods and exercises were developed using Protégé 4.3.0 and were retrieved by running Simple Protocol and Resource Description Framework Query Language (SPARQL) queries. Python 3 is used as the backend language for ontology and interface integration. Frontend developed using Tkinter GUI in Python 3 and is presented for the users to ease the interaction with the system. Two ontological files of Foods and Exercises are loaded and tested for consistency using the HermiT reasoner with the aid of Owlready2. Accuracy and Correctness are checked by addressing the competency questions and by domain experts’ inspections.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116713858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Attention-Based Bidirectional Long Short Term Memory Networks Combine with Phrase Convolution Layer for Relation Extraction 基于注意的双向长短期记忆网络结合短语卷积层进行关系提取

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664707

Chuangmin Xie, Degang Chen, Hao Shi, Mingyu Fan

{"title":"Attention-Based Bidirectional Long Short Term Memory Networks Combine with Phrase Convolution Layer for Relation Extraction","authors":"Chuangmin Xie, Degang Chen, Hao Shi, Mingyu Fan","doi":"10.1109/SLAAI-ICAI54477.2021.9664707","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664707","url":null,"abstract":"Relation Extraction (RE) is one of the most important tasks in Natural Language Processing (NLP). In recent years, with the development of deep learning, a variety of deep neural networks, such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory Network (LSTM), have been used in relation extraction and made significant progress. Moreover, LSTM has become the mainstream model in the field of NLP due to its better long term dependencies capture capability than CNN. However, the ability of LSTM to capture long term dependencies is still limited. In order to solve this problem, we propose a phrase convolution structure. The structure can extract the phrase-level features of the sentence, and the sentence-level features can be further extracted after the features are input into LSTM. We believe that this actually enhances the ability of LSTM to capture long term dependencies. Our experiments on SemEva1-2010 Task 8 dataset show that the performance of our model is better than most existing models.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Intelligent Personal Research Assistant 智能个人研究助理

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664714

A. Karunananda, Thushari P. Silva, Dewmal Handapangoda, Sadika Sumanapala

{"title":"Intelligent Personal Research Assistant","authors":"A. Karunananda, Thushari P. Silva, Dewmal Handapangoda, Sadika Sumanapala","doi":"10.1109/SLAAI-ICAI54477.2021.9664714","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664714","url":null,"abstract":"Research is popularly known as a process. Many undergraduate, master and even doctoral students find it challenging to go through the research process comfortably. In this sense, novice researchers find pitfalls in the systematic literature review, research problem definition, methodology formulation, and evaluation. At present, some software solutions are available for supporting only specific steps such as literature review, data collection and data analysis. In filling this gap, we have developed an Intelligent Personal Research Assistant, InPRA, which can support the complete research process and scientific writing. InPRA has been powered by language processing, semantic analysis based on topic modelling, and machine learning in artificial intelligence. Based on the profile of a research student, InPRA recommends literature, guide through a systematic literature review leading to the definition of a research problem, formulating a methodology and executing it to generate a conclusion based on an evaluation. InPRA has a distinct feature to guide through writing papers and a thesis incrementally while conducting the research. Based on the evaluation results, the relatedness of the articles generated by InPRA is higher than traditional keyword-based searching. A supervisor can also use this software to monitor and keep track of the progress of their research student.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123749577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Daily Minimum and Maximum Temperature Forecasting in Sri Lanka: An Artificial Neural Network Approach 斯里兰卡日最低和最高气温预报:人工神经网络方法

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664708

Prabodha Chandrapala, N. Yapage, Meril Mendis

{"title":"Daily Minimum and Maximum Temperature Forecasting in Sri Lanka: An Artificial Neural Network Approach","authors":"Prabodha Chandrapala, N. Yapage, Meril Mendis","doi":"10.1109/SLAAI-ICAI54477.2021.9664708","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664708","url":null,"abstract":"The National Meteorological Center, Department of Meteorology, Sri Lanka is not currently using technologically advanced methods in forecasting daily minimum and maximum temperature of selected locations in the country. In the city weather forecast, they mainly focus on ten cities namely, Anuradhapura, Badulla, Batticaloa, Colombo, Galle, Hambantota, Jaffna, Kandy, Ratnapura, and Trincomalee, covering the entire island. Motivated by the requirement for a sophisticated forecasting technique, we introduce an Artificial Neural Network (ANN) approach for this problem using previous weather data as inputs from more than ten locations in Sri Lanka over ten years (2010-2019). The data used in this work were obtained from the Department of Meteorology, Sri Lanka. A three-layer (input, hidden and output) ANN having appropriate number of nodes in each layer and with the Ward architecture was constructed which uses three activation functions (Gaussian, Gaussian complement, and hyperbolic tangent) in the hidden layer. The model was validated using the k-fold cross-validation procedure. The results, that is, daily minimum and maximum temperature, were obtained using the R software package (4.0.3 version). It was observed that the predicted values were very homogeneous compared to the real values with a small error and this error was reduced using the gradient descent method. We further investigated how various choices of the number of hidden neurons and the epochs affect these results. It was found that the best number of neurons in the hidden layer was twenty one and if the number of epochs was increased the error was approaching zero. A close agreement between the real and predicted temperature values were observed in this work.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132319666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0