Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982477
P. Fehér, Krisztián Varga
Incumbent financial institutions are continuously challenged by small fintech startups and big technology enterprises in finding ways to offer new, customer centric, and most importantly digital services. This paper is focusing on the investing services. The presented research is using Design Thinking methodology to identify challenges of investing activities and finding digital ways to answer these challenges. Our target customer group were those, who are not investing right now, as we wanted to identify what are their barriers of entering the investment market. The research sample focuses on educated, young generations. Although the financial background of the sample provides a good opportunity of investments, alternate use of savings is more widespread, and the general behavior of the analyzed generation requires liquid assets. The qualitative answers highlight the necessity of knowledge, trust and external expertise for investing decisions. The research generated four main possible products for the non-investing segment in order to gain their trust, educate them or to offer them an opportunity that does not need huge amount of money in investment.
{"title":"Identifying digital investing services using design thinking methodology","authors":"P. Fehér, Krisztián Varga","doi":"10.1109/SKIMA47702.2019.8982477","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982477","url":null,"abstract":"Incumbent financial institutions are continuously challenged by small fintech startups and big technology enterprises in finding ways to offer new, customer centric, and most importantly digital services. This paper is focusing on the investing services. The presented research is using Design Thinking methodology to identify challenges of investing activities and finding digital ways to answer these challenges. Our target customer group were those, who are not investing right now, as we wanted to identify what are their barriers of entering the investment market. The research sample focuses on educated, young generations. Although the financial background of the sample provides a good opportunity of investments, alternate use of savings is more widespread, and the general behavior of the analyzed generation requires liquid assets. The qualitative answers highlight the necessity of knowledge, trust and external expertise for investing decisions. The research generated four main possible products for the non-investing segment in order to gain their trust, educate them or to offer them an opportunity that does not need huge amount of money in investment.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116794075","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}
Pub Date : 2019-08-01DOI: 10.1109/skima47702.2019.8982537
{"title":"SKIMA 2019 Welcome","authors":"","doi":"10.1109/skima47702.2019.8982537","DOIUrl":"https://doi.org/10.1109/skima47702.2019.8982537","url":null,"abstract":"","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126290795","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982476
R. Koirala, K. Dahal, S. Matalonga, Rameshwar Rijal
Blockchain technology as a foundation of distributed ledger offers an innovative platform for transparent and efficient transaction in Reverse Auction Bidding process in a supply chain for procuring carriers. This research work provides background and motivation for the use of Blockchain in such domains. A supply chain model is realized by deploying a smart contract in Blockchain to procure carrier. The model considers multi-attribute of the carriers while procuring one through the reverse auction bidding process. This research work validates the Blockchain-enabled supply chain model by simulating a supply chain proposed for a Dairy Company. Data to calibrate the simulation was taken from a published case study on Reverse Auctions in the supply chain. The result shows that the model is a feasible scheme and its features will offset the challenges of current RAB process making it more efficient and transparent.
{"title":"A Supply Chain Model with Blockchain-Enabled Reverse Auction Bidding Process for Transparency and Efficiency","authors":"R. Koirala, K. Dahal, S. Matalonga, Rameshwar Rijal","doi":"10.1109/SKIMA47702.2019.8982476","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982476","url":null,"abstract":"Blockchain technology as a foundation of distributed ledger offers an innovative platform for transparent and efficient transaction in Reverse Auction Bidding process in a supply chain for procuring carriers. This research work provides background and motivation for the use of Blockchain in such domains. A supply chain model is realized by deploying a smart contract in Blockchain to procure carrier. The model considers multi-attribute of the carriers while procuring one through the reverse auction bidding process. This research work validates the Blockchain-enabled supply chain model by simulating a supply chain proposed for a Dairy Company. Data to calibrate the simulation was taken from a published case study on Reverse Auctions in the supply chain. The result shows that the model is a feasible scheme and its features will offset the challenges of current RAB process making it more efficient and transparent.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134514469","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982419
Shabnam Sabah, Sara Anwar, Sadia Afroze, Md. Abulkalam Azad, Swakkhar Shatabda, D. Farid
Big data mining is one of the major challenging research issues in the field of machine learning for data mining applications in this present digital era. Big data consists of 3V’s: (1) volume - massive amount of data/too many bytes, (2) velocity - high speed streaming data/too high a rate, and (3) variety - data are coming from different sources/too many sources. Collecting and managing real-life big data is a difficult task, as big data is so big that we cannot keep all the data together in a single machine. Therefore, we need advanced relational database management systems with parallel computing to deal with big data. Knowledge mining from big data employing traditional machine learning and data mining techniques is a big issue and attract computational intelligent researcher in this area. In this paper, we have used the decision tree (DT) induction method for mining big data. Decision tree induction is one of the most preferable and well-known supervised learning technique, which is a top-down recursive divide and conquer algorithm and require little prior knowledge for constructing a classifier. The traditional DT algorithms like Iterative Dichotomiser 3 (ID3), C4.5 (a successor of ID3 algorithm), Classification and Regression Trees (CART) are generally built for mining relatively small datasets. So, we need a more scalable decision tree learning approach for mining big data. In this paper, we have engendered several trees employing two scalable decision tree algorithms: RainForest Tree and Bootstrapped Optimistic Algorithm for Tree construction (BOAT) using seven benchmark datasets from Keel Repository and UCI Machine Learning repository. We have compared the performance of RainForest and BOAT algorithms. Also, we have proposed a decision tree merging approach, as decision tree merging is a very complex and challenging task.
{"title":"Big Data with Decision Tree Induction","authors":"Shabnam Sabah, Sara Anwar, Sadia Afroze, Md. Abulkalam Azad, Swakkhar Shatabda, D. Farid","doi":"10.1109/SKIMA47702.2019.8982419","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982419","url":null,"abstract":"Big data mining is one of the major challenging research issues in the field of machine learning for data mining applications in this present digital era. Big data consists of 3V’s: (1) volume - massive amount of data/too many bytes, (2) velocity - high speed streaming data/too high a rate, and (3) variety - data are coming from different sources/too many sources. Collecting and managing real-life big data is a difficult task, as big data is so big that we cannot keep all the data together in a single machine. Therefore, we need advanced relational database management systems with parallel computing to deal with big data. Knowledge mining from big data employing traditional machine learning and data mining techniques is a big issue and attract computational intelligent researcher in this area. In this paper, we have used the decision tree (DT) induction method for mining big data. Decision tree induction is one of the most preferable and well-known supervised learning technique, which is a top-down recursive divide and conquer algorithm and require little prior knowledge for constructing a classifier. The traditional DT algorithms like Iterative Dichotomiser 3 (ID3), C4.5 (a successor of ID3 algorithm), Classification and Regression Trees (CART) are generally built for mining relatively small datasets. So, we need a more scalable decision tree learning approach for mining big data. In this paper, we have engendered several trees employing two scalable decision tree algorithms: RainForest Tree and Bootstrapped Optimistic Algorithm for Tree construction (BOAT) using seven benchmark datasets from Keel Repository and UCI Machine Learning repository. We have compared the performance of RainForest and BOAT algorithms. Also, we have proposed a decision tree merging approach, as decision tree merging is a very complex and challenging task.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477331","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982474
S. Jilani, H. Ugail, Andrew Logan
The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose.A two-category (Pakistani vs Non-Pakistani) task was used in combination with Deep learning (ResNet) based and VGG-based pre-trained models. A series of experiments were conducted using ResNet-50, ResNet-101, ResNet-152, VGG-Face, VGG-16 and VGG-19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet-50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGG-based models (VGG-16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve >90% accuracy on judgements of ethnicity.
{"title":"The Computer Nose Best","authors":"S. Jilani, H. Ugail, Andrew Logan","doi":"10.1109/SKIMA47702.2019.8982474","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982474","url":null,"abstract":"The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose.A two-category (Pakistani vs Non-Pakistani) task was used in combination with Deep learning (ResNet) based and VGG-based pre-trained models. A series of experiments were conducted using ResNet-50, ResNet-101, ResNet-152, VGG-Face, VGG-16 and VGG-19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet-50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGG-based models (VGG-16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve >90% accuracy on judgements of ethnicity.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134239790","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982402
Ehtzaz Chaudhry, A. Noreika, L. You, Jian-Jun Zhang, Jian Chang, H. Ugail, Alexander Malyshev, A. Carriazo, A. Iglesias, Z. Habib, Allah Bux Sargano, H. Haron
This paper presents a partial differential equation (PDE)-based surface modelling and simulation framework for lily flowers. We use a PDE-based surface modelling technique to represent shape of a lily flower and PDE-based dynamic simulation to animate blossom and decay processes of lily flowers. To this aim, we first automatically construct the geometry of lily flowers from photos to obtain feature curves. Second, we apply a PDE-based surface modelling technique to generate sweeping surfaces to obtain geometric models of the flowers. Then, we use a physics-driven and data-based method and introduce the flower shapes at the initial and final positions into our proposed dynamic deformation model to generate a realistic deformation of flower blossom and decay. The results demonstrate that our proposed technique can create realistic flower models and their movements and shape changes against time efficiently with a small data size.
{"title":"Modelling and Simulation of Lily flowers using PDE Surfaces","authors":"Ehtzaz Chaudhry, A. Noreika, L. You, Jian-Jun Zhang, Jian Chang, H. Ugail, Alexander Malyshev, A. Carriazo, A. Iglesias, Z. Habib, Allah Bux Sargano, H. Haron","doi":"10.1109/SKIMA47702.2019.8982402","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982402","url":null,"abstract":"This paper presents a partial differential equation (PDE)-based surface modelling and simulation framework for lily flowers. We use a PDE-based surface modelling technique to represent shape of a lily flower and PDE-based dynamic simulation to animate blossom and decay processes of lily flowers. To this aim, we first automatically construct the geometry of lily flowers from photos to obtain feature curves. Second, we apply a PDE-based surface modelling technique to generate sweeping surfaces to obtain geometric models of the flowers. Then, we use a physics-driven and data-based method and introduce the flower shapes at the initial and final positions into our proposed dynamic deformation model to generate a realistic deformation of flower blossom and decay. The results demonstrate that our proposed technique can create realistic flower models and their movements and shape changes against time efficiently with a small data size.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125793413","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982486
Hayat Ullah, I. Mehmood
In today’s industries, automation, reliability, robustness and accuracy are pivotal problem to cut costs and increase productivity and quality. Visual sensor networks are vital control and monitoring tools for continues, on-line imaging and real time image processing in production and plant process. Most of the industrial videos are captured in hazy weather and usually degraded by suspended particles of atmosphere, such as smoke, fog, rain, and snow, which limits the visual quality of image. This hinders the ability of artificial intelligent driven systems to achieve automation, reliability and accuracy. Recovery of the clear visuals from the input hazy videos is challenging problem. Instead of relying on explicitly estimating the key component of atmospheric scattering model, we present end-to-end CNN model, which directly recovers the clear images from hazy images. This end-to-end architecture makes it an ideal pre-processing tool into other deep models for increasing the efficiency of various computer vision tasks in real time systems, such as Retina-Net for object detection, ResNet for object recognition. Experimental results demonstrate the effectiveness and robustness of proposed framework by outperforming the stat-of-the-art approaches in terms of time complexity and visual quality.
{"title":"Real-Time Video Dehazing for Industrial Image Processing","authors":"Hayat Ullah, I. Mehmood","doi":"10.1109/SKIMA47702.2019.8982486","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982486","url":null,"abstract":"In today’s industries, automation, reliability, robustness and accuracy are pivotal problem to cut costs and increase productivity and quality. Visual sensor networks are vital control and monitoring tools for continues, on-line imaging and real time image processing in production and plant process. Most of the industrial videos are captured in hazy weather and usually degraded by suspended particles of atmosphere, such as smoke, fog, rain, and snow, which limits the visual quality of image. This hinders the ability of artificial intelligent driven systems to achieve automation, reliability and accuracy. Recovery of the clear visuals from the input hazy videos is challenging problem. Instead of relying on explicitly estimating the key component of atmospheric scattering model, we present end-to-end CNN model, which directly recovers the clear images from hazy images. This end-to-end architecture makes it an ideal pre-processing tool into other deep models for increasing the efficiency of various computer vision tasks in real time systems, such as Retina-Net for object detection, ResNet for object recognition. Experimental results demonstrate the effectiveness and robustness of proposed framework by outperforming the stat-of-the-art approaches in terms of time complexity and visual quality.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301752","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982467
Guillermo Díaz, A. Iglesias
Evolutionary computation covers the family of artificial intelligence techniques inspired by nature and biological evolution. These methods, such as swarm intelligence, may have a very positive impact on video games, for instance, for the design of Non-Player Characters (NPCs) to obtain a realistic intelligent behavior in a simple way. To this aim, we describe an evolutionary behavioral design of NPCs using particle swarm optimization in a first-person shooter video game. Several computer experiments have been carried out to analyze the feasibility and performance of this approach. Our experimental results show that the proposed method performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.
{"title":"Evolutionary Behavioral Design of Non-Player Characters in a FPS Video Game Through Particle Swarm Optimization","authors":"Guillermo Díaz, A. Iglesias","doi":"10.1109/SKIMA47702.2019.8982467","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982467","url":null,"abstract":"Evolutionary computation covers the family of artificial intelligence techniques inspired by nature and biological evolution. These methods, such as swarm intelligence, may have a very positive impact on video games, for instance, for the design of Non-Player Characters (NPCs) to obtain a realistic intelligent behavior in a simple way. To this aim, we describe an evolutionary behavioral design of NPCs using particle swarm optimization in a first-person shooter video game. Several computer experiments have been carried out to analyze the feasibility and performance of this approach. Our experimental results show that the proposed method performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115781146","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982452
Ahmad Yahya Dawod, N. Chakpitak
Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Fingerspelling recognition method from isolate sign language has attracted research interest in computer vision and human-computer interaction based on a novel technique. The essential for real-time recognition of isolate sign language has grown with the emergence of better-capturing devices such as Kinect sensors. The purpose of this paper is to design a user independent framework for automatic recognition of American Sign Language which can recognize several one-handed dynamic isolated signs and interpreting their meaning. We built datasets as a raw data for alphabets (A–Z) or numbers (1–20) by used left-hand the 3D point (XL, YL, ZL) or switch by right-hand (XR, YR, ZR) centroid as one of contribution. The proposed approach was tested for gestures that involve left-hand or right-hand and was compared with other approach and gave better accuracy. Two machine learning methods are involved like Hidden Conditional Random Field (HCRF), and Random Decision Forest (RDF) for the classification part. The third contribution based on low lighting condition and cluttered background. In this research work is achieved for recognition accuracy over 99.7%.
{"title":"Novel Technique for Isolated Sign Language Based on Fingerspelling Recognition","authors":"Ahmad Yahya Dawod, N. Chakpitak","doi":"10.1109/SKIMA47702.2019.8982452","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982452","url":null,"abstract":"Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Fingerspelling recognition method from isolate sign language has attracted research interest in computer vision and human-computer interaction based on a novel technique. The essential for real-time recognition of isolate sign language has grown with the emergence of better-capturing devices such as Kinect sensors. The purpose of this paper is to design a user independent framework for automatic recognition of American Sign Language which can recognize several one-handed dynamic isolated signs and interpreting their meaning. We built datasets as a raw data for alphabets (A–Z) or numbers (1–20) by used left-hand the 3D point (XL, YL, ZL) or switch by right-hand (XR, YR, ZR) centroid as one of contribution. The proposed approach was tested for gestures that involve left-hand or right-hand and was compared with other approach and gave better accuracy. Two machine learning methods are involved like Hidden Conditional Random Field (HCRF), and Random Decision Forest (RDF) for the classification part. The third contribution based on low lighting condition and cluttered background. In this research work is achieved for recognition accuracy over 99.7%.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"1117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116066356","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}
Pub Date : 2019-08-01DOI: 10.1109/SKIMA47702.2019.8982488
A. Imad
Remodeling the hospitality industry through artificial intelligence (AI) – that uses big data analytics and complex machine learning – is a concept that will help the industry to leapfrog to the next level. The notion put forward in this paper is to develop a framework to utilize machine learning analyses the multi-channel user data for efficient decision making to enrich the customer experience and to provide the maximum revenue to the vendor. We propose strategies to infer customer behaviors by capturing otherwise salient information – e.g. through the various digital footprints. Feeding such analytics to a suitably trained collection of machine learning algorithms called the “Digital Operations Manager” helps to automate complex decision making, removing human error and bias. The proposed AI system, if appropriately deployed within a hospitality industry environment, is thought to bring out a significant gain in the user choice and experience as well as efficiency in resource management and revenue optimization.
{"title":"Remodeling Hospitality Industry through Artificial Intelligence","authors":"A. Imad","doi":"10.1109/SKIMA47702.2019.8982488","DOIUrl":"https://doi.org/10.1109/SKIMA47702.2019.8982488","url":null,"abstract":"Remodeling the hospitality industry through artificial intelligence (AI) – that uses big data analytics and complex machine learning – is a concept that will help the industry to leapfrog to the next level. The notion put forward in this paper is to develop a framework to utilize machine learning analyses the multi-channel user data for efficient decision making to enrich the customer experience and to provide the maximum revenue to the vendor. We propose strategies to infer customer behaviors by capturing otherwise salient information – e.g. through the various digital footprints. Feeding such analytics to a suitably trained collection of machine learning algorithms called the “Digital Operations Manager” helps to automate complex decision making, removing human error and bias. The proposed AI system, if appropriately deployed within a hospitality industry environment, is thought to bring out a significant gain in the user choice and experience as well as efficiency in resource management and revenue optimization.","PeriodicalId":245523,"journal":{"name":"2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116403960","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}
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