Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970780
K. Nisar, Ag. Asri Ag. Ibrahim, Yong-Jin Park, Yeoh Keng Hzou, Shuaib K. Memon, Noureen Naz, I. Welch
Smart Home (SH) is a house or an apartment equipped with advanced automation technologies to provide the occupants with intelligent monitoring and actionable information that can be situation specific. Recent research indicates that the population over the age of 60 is growing at an alarming rate, which is estimated that by 2050 this particular group will have globally increased by over 50%. With such an increase, the sense of eldercare is being emphasized among nowadays and Smart Home is undoubtedly a promising solution to the problem. This research paper involves the SH architecture that includes sensors, data communication, and data integration. The system collects movement based activity data from the elderly using Radio Frequency Identification (RFID) technology. The research also discusses why RFID is chosen among other sensors. RFID is the use of radio waves to read and capture information stored on a tag attached to an object. A tag can be read from up to several feet away and does not need to be within direct line-of-sight of the reader to be tracked. The RFID system is made up of two parts such as a label and a reader. This paper presents a study of why movement based activity detection can be used in the longterm to help elderly people living alone in a Smart Home. The work focuses on the need to manage the data and how the system must be maintained.
{"title":"Indoor Roaming Activity Detection and Analysis of Elderly People using RFID Technology","authors":"K. Nisar, Ag. Asri Ag. Ibrahim, Yong-Jin Park, Yeoh Keng Hzou, Shuaib K. Memon, Noureen Naz, I. Welch","doi":"10.1109/AiDAS47888.2019.8970780","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970780","url":null,"abstract":"Smart Home (SH) is a house or an apartment equipped with advanced automation technologies to provide the occupants with intelligent monitoring and actionable information that can be situation specific. Recent research indicates that the population over the age of 60 is growing at an alarming rate, which is estimated that by 2050 this particular group will have globally increased by over 50%. With such an increase, the sense of eldercare is being emphasized among nowadays and Smart Home is undoubtedly a promising solution to the problem. This research paper involves the SH architecture that includes sensors, data communication, and data integration. The system collects movement based activity data from the elderly using Radio Frequency Identification (RFID) technology. The research also discusses why RFID is chosen among other sensors. RFID is the use of radio waves to read and capture information stored on a tag attached to an object. A tag can be read from up to several feet away and does not need to be within direct line-of-sight of the reader to be tracked. The RFID system is made up of two parts such as a label and a reader. This paper presents a study of why movement based activity detection can be used in the longterm to help elderly people living alone in a Smart Home. The work focuses on the need to manage the data and how the system must be maintained.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129304325","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970933
I. A. Mohtar, Nurhariyanni Ramli, Zaaba Ahmad
The retail quality of mangosteen depends on the harvesting of the fruit at the right ripening stage. Mangosteen harvested too early or too late will compromise the quality and consequently affect the yield for the season. The ability to automate the classification of the ripening stage of mangosteen will help the farmers during the harvesting phase to determine under-matured, matured and over-matured mangosteen. This study proposes a Convolutional Neural Network architecture utilizing the V3 Inception model, to classify the ripening stages of mangosteen. A total of 800 images were used to train the model. The model was able to achieve training accuracy of 99%, validation accuracy of 97% and testing accuracy of 91.9% after 500 epochs. The precision, recall and F1 score achieved were 0.88, 0.96, and 0.92 respectively. As a conclusion, the V3 Inception model is able to classify the ripening stages of mangosteen. It is hoped that this study will initiate the commercialization of this effort to assist the mangosteen industry.
{"title":"Automatic Classification of Mangosteen Ripening Stages using Deep Learning","authors":"I. A. Mohtar, Nurhariyanni Ramli, Zaaba Ahmad","doi":"10.1109/AiDAS47888.2019.8970933","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970933","url":null,"abstract":"The retail quality of mangosteen depends on the harvesting of the fruit at the right ripening stage. Mangosteen harvested too early or too late will compromise the quality and consequently affect the yield for the season. The ability to automate the classification of the ripening stage of mangosteen will help the farmers during the harvesting phase to determine under-matured, matured and over-matured mangosteen. This study proposes a Convolutional Neural Network architecture utilizing the V3 Inception model, to classify the ripening stages of mangosteen. A total of 800 images were used to train the model. The model was able to achieve training accuracy of 99%, validation accuracy of 97% and testing accuracy of 91.9% after 500 epochs. The precision, recall and F1 score achieved were 0.88, 0.96, and 0.92 respectively. As a conclusion, the V3 Inception model is able to classify the ripening stages of mangosteen. It is hoped that this study will initiate the commercialization of this effort to assist the mangosteen industry.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643651","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970975
A. Abadi, Sumarna
Heart disease (cardiovascular disease) is any condition that causes interference with the heart. This study aims to determine the classification of heart disease based on phonocardiogram signals using the fuzzy system. The data used are the heart sound recordings from patients with normal hearts and cardiovascular abnormalities, which were recorded using a phonocardiogram device. The signal extraction process was carried out using wavelet decomposition mother Haar to produce features as input variables. While the output produced is a classification for heart conditions (normal or abnormal). Furthermore, the singular value decomposition method was utilized to determine the consequence parameters of the first-order Takagi-Sugeno-Kang (TSK) fuzzy rule. Fuzzy C-Means Clustering (FCM) was also used to optimize the number of fuzzy rules. As for the defuzzification process, the weight average method was used. The results showed that the accuracy and specificity of the training and testing data are better compared to the Mamdani and the radial basis function neural network (RBFNN) methods.
{"title":"Construction of Fuzzy System for Classification of Heart Disease Based on Phonocardiogram Signal","authors":"A. Abadi, Sumarna","doi":"10.1109/AiDAS47888.2019.8970975","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970975","url":null,"abstract":"Heart disease (cardiovascular disease) is any condition that causes interference with the heart. This study aims to determine the classification of heart disease based on phonocardiogram signals using the fuzzy system. The data used are the heart sound recordings from patients with normal hearts and cardiovascular abnormalities, which were recorded using a phonocardiogram device. The signal extraction process was carried out using wavelet decomposition mother Haar to produce features as input variables. While the output produced is a classification for heart conditions (normal or abnormal). Furthermore, the singular value decomposition method was utilized to determine the consequence parameters of the first-order Takagi-Sugeno-Kang (TSK) fuzzy rule. Fuzzy C-Means Clustering (FCM) was also used to optimize the number of fuzzy rules. As for the defuzzification process, the weight average method was used. The results showed that the accuracy and specificity of the training and testing data are better compared to the Mamdani and the radial basis function neural network (RBFNN) methods.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271066","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970882
Yan Soon Weei, H. S. Pheng
The abnormal growth of cells in the lungs leads to the development of nodules and the overgrowth of lung nodules will eventually form a cancerous cell. Detection of lung nodules in the early stage is vital in such a way that proper treatments can be applied before the lung nodules grow into lethal lung cancer. In recent decades, machine learning has been widely used in the computer aided system to provide second opinion to the radiologists in the detection of abnormality on medical images. The aim of this paper is to implement a machine learning algorithm in the classification and enhancement of lung nodules on computed tomography (CT) images. The classification model – Massive-Training Self-Organizing Map and Learning Vector Quantization (MTSOM-LVQ) is implemented to classify the sub-regions based on the teaching Gaussian values. Each sub-region is associated with its teaching value generated by using Gaussian distribution function. The results show that MTSOM-LVQ is able to enhance nodules and suppressing non-nodules on CT images. Adjustment on the parameters such as map size, training iteration and size of the training sample would affect the performance of the MTSOMLVQ. Besides, the performance of the MTSOM-LVQ is validated and 90% classification sensitivity is achieved. As a conclusion, the training accuracy can be further improved by choosing the optimized parameters for MTSOM-LVQ in future research.
{"title":"Lung Nodules Classification Using Massive-Training Self-Organizing Map and Learning Vector Quantization","authors":"Yan Soon Weei, H. S. Pheng","doi":"10.1109/AiDAS47888.2019.8970882","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970882","url":null,"abstract":"The abnormal growth of cells in the lungs leads to the development of nodules and the overgrowth of lung nodules will eventually form a cancerous cell. Detection of lung nodules in the early stage is vital in such a way that proper treatments can be applied before the lung nodules grow into lethal lung cancer. In recent decades, machine learning has been widely used in the computer aided system to provide second opinion to the radiologists in the detection of abnormality on medical images. The aim of this paper is to implement a machine learning algorithm in the classification and enhancement of lung nodules on computed tomography (CT) images. The classification model – Massive-Training Self-Organizing Map and Learning Vector Quantization (MTSOM-LVQ) is implemented to classify the sub-regions based on the teaching Gaussian values. Each sub-region is associated with its teaching value generated by using Gaussian distribution function. The results show that MTSOM-LVQ is able to enhance nodules and suppressing non-nodules on CT images. Adjustment on the parameters such as map size, training iteration and size of the training sample would affect the performance of the MTSOMLVQ. Besides, the performance of the MTSOM-LVQ is validated and 90% classification sensitivity is achieved. As a conclusion, the training accuracy can be further improved by choosing the optimized parameters for MTSOM-LVQ in future research.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130110929","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970911
Ezzatul Akmal Kamaru Zaman, N. Rahmat, Azlin Ahmad, Nur Huda Nabihan Binti Md Shahri, Mohd Najib Ismail
Travel agencies set new prices on travel packages based on their experiences by analyzing the trend on holiday and festive season. However, they find it hard to set and predict exact travel packages with minimum prices to be offered for the upcoming years. Prices keep changing due to other reasons rather than the holiday and festive season. This research paper applied data analytics which is divided into two parts, 1) descriptive analytics to facilitate the agencies to have better insights of the data and 2) predictive analytics for price forecasting. Visualization is a part of descriptive analytics where dispersion and correlation of data are produced to gain insight of data. Meanwhile, in the predictive analytics part, Linear Regression and Multiple Linear Regression models are applied to predict the price of travel packages. Different parameter settings are applied to optimize the score of R-square. Hence, the final result of 0.9346 R-square is achieved by applying Multiple Linear Regression with all variables are taken into consideration.
{"title":"Data Analytics on Price Prediction of Travelling Package using Regression Models","authors":"Ezzatul Akmal Kamaru Zaman, N. Rahmat, Azlin Ahmad, Nur Huda Nabihan Binti Md Shahri, Mohd Najib Ismail","doi":"10.1109/AiDAS47888.2019.8970911","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970911","url":null,"abstract":"Travel agencies set new prices on travel packages based on their experiences by analyzing the trend on holiday and festive season. However, they find it hard to set and predict exact travel packages with minimum prices to be offered for the upcoming years. Prices keep changing due to other reasons rather than the holiday and festive season. This research paper applied data analytics which is divided into two parts, 1) descriptive analytics to facilitate the agencies to have better insights of the data and 2) predictive analytics for price forecasting. Visualization is a part of descriptive analytics where dispersion and correlation of data are produced to gain insight of data. Meanwhile, in the predictive analytics part, Linear Regression and Multiple Linear Regression models are applied to predict the price of travel packages. Different parameter settings are applied to optimize the score of R-square. Hence, the final result of 0.9346 R-square is achieved by applying Multiple Linear Regression with all variables are taken into consideration.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121647539","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970890
Arun Kumar Kalakanti, Shivani Verma, T. Paul, Takufumi Yoshida
Vehicle Routing Problem (VRP) is a well-known NP-hard combinatorial optimization problem at the heart of the transportation and logistics research. VRP can be exactly solved only for small instances of the problem with conventional methods. Traditionally this problem has been solved using heuristic methods for large instances even though there is no guarantee of optimality. Efficient solution adopted to VRP may lead to significant savings per year in large transportation and logistics systems. Much of the recent works using Reinforcement Learning are computationally intensive and face the three curse of dimensionality: explosions in state and action spaces and high stochasticity i.e., large number of possible next states for a given state action pair. Also, recent works on VRP don’t consider the realistic simulation settings of customer environments, stochastic elements and scalability aspects as they use only standard Solomon benchmark instances of at most 100 customers. In this work, Reinforcement Learning Solver for Vehicle Routing Problem (RL SolVeR Pro) is proposed wherein the optimal route learning problem is cast as a Markov Decision Process (MDP). The curse of dimensionality of RL is also overcome by using two-phase solver with geometric clustering. Also, realistic simulation for VRP was used to validate the effectiveness and applicability of the proposed RL SolVeR Pro under various conditions and constraints. Our simulation results suggest that our proposed method is able to obtain better or same level of results, compared to the two best-known heuristics: Clarke-Wright Savings and Sweep Heuristic. The proposed RL Solver can be applied to other variants of the VRP and has the potential to be applied more generally to other combinatorial optimization problems.
{"title":"RL SolVeR Pro: Reinforcement Learning for Solving Vehicle Routing Problem","authors":"Arun Kumar Kalakanti, Shivani Verma, T. Paul, Takufumi Yoshida","doi":"10.1109/AiDAS47888.2019.8970890","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970890","url":null,"abstract":"Vehicle Routing Problem (VRP) is a well-known NP-hard combinatorial optimization problem at the heart of the transportation and logistics research. VRP can be exactly solved only for small instances of the problem with conventional methods. Traditionally this problem has been solved using heuristic methods for large instances even though there is no guarantee of optimality. Efficient solution adopted to VRP may lead to significant savings per year in large transportation and logistics systems. Much of the recent works using Reinforcement Learning are computationally intensive and face the three curse of dimensionality: explosions in state and action spaces and high stochasticity i.e., large number of possible next states for a given state action pair. Also, recent works on VRP don’t consider the realistic simulation settings of customer environments, stochastic elements and scalability aspects as they use only standard Solomon benchmark instances of at most 100 customers. In this work, Reinforcement Learning Solver for Vehicle Routing Problem (RL SolVeR Pro) is proposed wherein the optimal route learning problem is cast as a Markov Decision Process (MDP). The curse of dimensionality of RL is also overcome by using two-phase solver with geometric clustering. Also, realistic simulation for VRP was used to validate the effectiveness and applicability of the proposed RL SolVeR Pro under various conditions and constraints. Our simulation results suggest that our proposed method is able to obtain better or same level of results, compared to the two best-known heuristics: Clarke-Wright Savings and Sweep Heuristic. The proposed RL Solver can be applied to other variants of the VRP and has the potential to be applied more generally to other combinatorial optimization problems.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127465123","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970865
Mohammad Shahrul Izham Sharifuddin, Sharifalillah Nordin, A. Ali
In this paper, we introduced a voice control intelligent wheelchair movement using Convolutional Neural Networks (CNNs). The intelligent wheelchair used four voice commands such as stop, go, left and right to assist disable people to move. Data are collected from google in the wav format. Mel-Frequency Cepstral Coefficient (MFCC) is applied to extract the command voice. The hardware used to deploy the system is Raspberry PI 3B+. The proposed method is using CNNs to classify the voice command and achieved excellent result with 95.30% accuracy. Therefore, the method can be commercialized and hopefully can give benefit to the disable society.
本文介绍了一种基于卷积神经网络(cnn)的语音控制智能轮椅运动。这款智能轮椅使用“停”、“走”、“左”、“右”等四种语音指令来帮助残疾人移动。数据以wav格式从google收集。使用Mel-Frequency倒谱系数(MFCC)提取命令语音。部署系统的硬件为Raspberry PI 3B+。该方法利用cnn对语音命令进行分类，准确率达到95.30%，取得了优异的效果。因此，该方法可以商业化，并有望为残疾人社会带来好处。
{"title":"Voice Control Intelligent Wheelchair Movement Using CNNs","authors":"Mohammad Shahrul Izham Sharifuddin, Sharifalillah Nordin, A. Ali","doi":"10.1109/AiDAS47888.2019.8970865","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970865","url":null,"abstract":"In this paper, we introduced a voice control intelligent wheelchair movement using Convolutional Neural Networks (CNNs). The intelligent wheelchair used four voice commands such as stop, go, left and right to assist disable people to move. Data are collected from google in the wav format. Mel-Frequency Cepstral Coefficient (MFCC) is applied to extract the command voice. The hardware used to deploy the system is Raspberry PI 3B+. The proposed method is using CNNs to classify the voice command and achieved excellent result with 95.30% accuracy. Therefore, the method can be commercialized and hopefully can give benefit to the disable society.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794197","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970757
Syed Muslim Jameel, M. Hashmani, Hitham Al Hussain, M. Rehman, Arif Budiman
Image Classification (IC) is most prominent among other Artificial Intelligence (AI) domains. Mainly, IC participates rigorously for the development of society in a variety of application areas such as finance, marketing, health, industrial automation, education, and safety and security. Typically, an IC model takes image input data and tunes itself as per the required application task and classify accordingly. Among the various categories of images, color image category is better due to the capability of capturing more details, which are essential for classification purpose. However, the modern world demands Realtime or online image classification, which involves Imagery Streams. The highly likely uncertainty in Imagery Streams is due to non-stationary environment, for example, certain features or class boundaries which are valid at one-time step are not adequate for another time step. These uncertainties in Imagery Streams have deleterious effects on IC models, which causes performance degradation in terms of accuracy or make IC models, not in further use. Therefore, to overcome these issues, IC models need to adapt to changes caused by uncertainties in Imagery Streams. This paper focuses on the understanding the possible scenarios of such uncertainties in Color Imagery Streams, investigates the deleterious effects due to changes in Color Imagery Streams and provides the possible mitigation approach to overcome the issues in IC models. The contribution of this research is the first step towards an adaptive model development to mitigate the deleterious effects of uncertainty in Color Imagery Streams. This model will benefit many application areas and will directly contribute to the daily life of a society.
{"title":"Deleterious Effects of Uncertainty in Color Imagery Streams on Classification Models","authors":"Syed Muslim Jameel, M. Hashmani, Hitham Al Hussain, M. Rehman, Arif Budiman","doi":"10.1109/AiDAS47888.2019.8970757","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970757","url":null,"abstract":"Image Classification (IC) is most prominent among other Artificial Intelligence (AI) domains. Mainly, IC participates rigorously for the development of society in a variety of application areas such as finance, marketing, health, industrial automation, education, and safety and security. Typically, an IC model takes image input data and tunes itself as per the required application task and classify accordingly. Among the various categories of images, color image category is better due to the capability of capturing more details, which are essential for classification purpose. However, the modern world demands Realtime or online image classification, which involves Imagery Streams. The highly likely uncertainty in Imagery Streams is due to non-stationary environment, for example, certain features or class boundaries which are valid at one-time step are not adequate for another time step. These uncertainties in Imagery Streams have deleterious effects on IC models, which causes performance degradation in terms of accuracy or make IC models, not in further use. Therefore, to overcome these issues, IC models need to adapt to changes caused by uncertainties in Imagery Streams. This paper focuses on the understanding the possible scenarios of such uncertainties in Color Imagery Streams, investigates the deleterious effects due to changes in Color Imagery Streams and provides the possible mitigation approach to overcome the issues in IC models. The contribution of this research is the first step towards an adaptive model development to mitigate the deleterious effects of uncertainty in Color Imagery Streams. This model will benefit many application areas and will directly contribute to the daily life of a society.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127771331","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}
The Supply Chain Forensics are latest revolution to save money and reduce the Risk Supply chain management has been developed over many centuries ago from ancient civilization. The review of literature reveals that the current Digital Supply Chain industry is more connected to IoT devices which is latest revolution and future development such as gathering information and tracking goods, has evolved in to smart Supply Chain with RFID based tagging and sensor based technologies connected to IoT (Internet of Things). The use of these devices gives accurate organizational and operational outcome even though there may be unidentified procedural inconsistencies that may weakens the smooth operational procedures. This paper explains about the vulnerabilities related Supply Chain industry 4.0. Even though the application of IoT today not only tracks the goods and services but also predicts helps in future analysis for Supply Chain Industry. The entire digitalized process may help in protecting and reducing losses but there may be vulnerabilities will be identified by digital forensics. The problem really comes into focus when IoT devices are connected in unsecure Supply Chain environment. That may further concern is the fractured digital supply chains that they are relying on by modern industry experts.
{"title":"IoT- Supply Chain Forensics and Vulnerabilities","authors":"Venkata Venugopal Rao Gudlur, Vikneswara Abirama Shanmugan, Sundresan Perumal, Radin Maya Saphira Radin Mohammed","doi":"10.1109/AiDAS47888.2019.8970765","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970765","url":null,"abstract":"The Supply Chain Forensics are latest revolution to save money and reduce the Risk Supply chain management has been developed over many centuries ago from ancient civilization. The review of literature reveals that the current Digital Supply Chain industry is more connected to IoT devices which is latest revolution and future development such as gathering information and tracking goods, has evolved in to smart Supply Chain with RFID based tagging and sensor based technologies connected to IoT (Internet of Things). The use of these devices gives accurate organizational and operational outcome even though there may be unidentified procedural inconsistencies that may weakens the smooth operational procedures. This paper explains about the vulnerabilities related Supply Chain industry 4.0. Even though the application of IoT today not only tracks the goods and services but also predicts helps in future analysis for Supply Chain Industry. The entire digitalized process may help in protecting and reducing losses but there may be vulnerabilities will be identified by digital forensics. The problem really comes into focus when IoT devices are connected in unsecure Supply Chain environment. That may further concern is the fractured digital supply chains that they are relying on by modern industry experts.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957826","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970881
S. Marzukhi, Zuraini Zainol, H. Muhamed, N. Awang, T. Sembok, Jowati Juhary
Industrial Revolution 4.0 is expected to improve the way of military training system. Most of the assistant systems use English for their Human Machine Interaction (HMI) such ‘SARA’ a virtual socially aware robot assistant which exclude Malay socio-emotional aspects. This scenario opens a suggestion, to internalize socio-emotional aspects based on Malay culture, custom and beliefs to military autonomous training systems (i.e. MIN@H) that can improve the ‘collaborative’ skills between Malaysian military personnel and the systems. Therefore, to increase the wisdom of the systems, they must have feature to capture information for their human users or helping human users to learn new knowledge and ensure the interaction is comfortable and engaging. For that reason, the systems must understand Malay language and be able to interpret emotion and expression behavior according to the Malay culture and custom, furthermore, the systems able to differentiate the level of user’s understanding and build a good rapport or feeling of harmony that makes communication possible or easy between the systems and users. This concept of the systems is referred as Malay Artificial Wisdom System (AWS). There are three fundamental aspects to achieve the AWS. First, to computationally model the conversational strategies and rapport between the system and human users based-on user’s understanding and system’s articulation. Second, to computationally model, recognize and synthesize the emotion and expression behavior according to the Malay culture, custom and beliefs. Third, the AWS can do analytical reasoning and responding in relation to falsehood analysis and users’ understanding level. Knowledge discovery and inference technique as well as HMI that cater the inputs and output of the MIN@H will be developed to accomplish the AWS concept. This program could embrace military training system in Malaysia to enhance military personnel skills and experts in various areas.
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