Pub Date : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180422
Ferda Saepulah, A. Santoso
This paper refers to the modeling of lateral stability control and braking torque allocation in electric vehicles solely from the brake-by-wire (BBW) system. The vehicle model uses a seven degree of freedom yaw plane model and a control scheme that makes the vehicle always stable when driving in predetermined road conditions. In addition, the case study of maintaining vehicle stability was carried out in two stages, namely, maintaining the yaw rate and side slip angle values of the vehicle and allocating braking torque to each tire using a Fuzzy-Sliding Mode controller (FSMC). The first stage uses the steering wheel angle as input and yaw moment as output. In addition, in the second stage it uses an anti-lock braking system (ABS) algorithm to control slip output and each braking device on each wheel will respond according to vehicle conditions. The analysis and results of the simulations performed illustrate an effective solution for autonomous lateral control or assisted lateral control. The response of FSMC has 16.61% better on steering input sine wave and 22.35% better on steering input step when compared to SMC without parameter optimization, making it more effective in applications in vehicle lateral stability control.
{"title":"Electric Vehicle Lateral Stability Control Design Based on Brake-By-Wire System Using Fuzzy-SMC","authors":"Ferda Saepulah, A. Santoso","doi":"10.1109/ISMODE56940.2022.10180422","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180422","url":null,"abstract":"This paper refers to the modeling of lateral stability control and braking torque allocation in electric vehicles solely from the brake-by-wire (BBW) system. The vehicle model uses a seven degree of freedom yaw plane model and a control scheme that makes the vehicle always stable when driving in predetermined road conditions. In addition, the case study of maintaining vehicle stability was carried out in two stages, namely, maintaining the yaw rate and side slip angle values of the vehicle and allocating braking torque to each tire using a Fuzzy-Sliding Mode controller (FSMC). The first stage uses the steering wheel angle as input and yaw moment as output. In addition, in the second stage it uses an anti-lock braking system (ABS) algorithm to control slip output and each braking device on each wheel will respond according to vehicle conditions. The analysis and results of the simulations performed illustrate an effective solution for autonomous lateral control or assisted lateral control. The response of FSMC has 16.61% better on steering input sine wave and 22.35% better on steering input step when compared to SMC without parameter optimization, making it more effective in applications in vehicle lateral stability control.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123005942","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180920
Narmeen H. Fathi, Y. Abbosh, D. Ali
In this paper, the detection, and localization of a hidden object in the human body using deep neural networks have been studied. To build a model, an electromagnetic simulator is employed. The model consists of four layers (skin-fat-muscle-bone) each of these layers has different conductivity and relative permittivity. Spherical shrapnel of different sizes 5mm, 10mm, and 15mm is supposed to be at various places in the model. The signal is directed at the model using a monopole ultra-wideband antenna, which is also used to pick up signals that are reflected back. In order to determine whether shrapnel is present or not, its size, and where it is located, the collected signals are analyzed using a deep neural network. The acquired results utilizing the suggested method are encouraging, with 90% success in shrapnel identification, 88% success in shrapnel sizing, and 78% success in shrapnel depth. More antennae could be used to improve performance.
{"title":"Hidden Object Recognition using Convolutional Neural Network","authors":"Narmeen H. Fathi, Y. Abbosh, D. Ali","doi":"10.1109/ISMODE56940.2022.10180920","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180920","url":null,"abstract":"In this paper, the detection, and localization of a hidden object in the human body using deep neural networks have been studied. To build a model, an electromagnetic simulator is employed. The model consists of four layers (skin-fat-muscle-bone) each of these layers has different conductivity and relative permittivity. Spherical shrapnel of different sizes 5mm, 10mm, and 15mm is supposed to be at various places in the model. The signal is directed at the model using a monopole ultra-wideband antenna, which is also used to pick up signals that are reflected back. In order to determine whether shrapnel is present or not, its size, and where it is located, the collected signals are analyzed using a deep neural network. The acquired results utilizing the suggested method are encouraging, with 90% success in shrapnel identification, 88% success in shrapnel sizing, and 78% success in shrapnel depth. More antennae could be used to improve performance.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126352657","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180994
Z. D. O. Permata, D. B. Sencaki, Afifuddin, M. Frederik, H. Priyadi, M. N. Putri, S. Arfah, Agustan, F. Alhasanah, L. Sumargana, R. Arifandri, N. Anatoly
Water resources are important to be managed in a sustainable manner. Changes to the land cover such as from forest to agriculture will affect the water quality and quantity of the whole watershed system. The Serayu watershed in Central Java is considered one of the critical watersheds in Indonesia due to the high erosion and sedimentation rate from the conversion of forested land to horticulture land. This paper presents a study of land cover change using the Sentine1-2 satellite images from 2018- 2019 to 2020-2021 using the machine learning method. The Sentine1-2 images have a temporal resolution of 10 days which is necessary because of the high cloud cover in the study area. Image classification using the Light Gradient Boosting yields an overall accuracy from the training and testing dataset of 1.0 and 0.929 for images 2018 – 2019 and 1.0 and 0.915 for images 2020 – 2021. Field verification upstream of the Serayu watershed shows a good agreement with the classification results, where discrepancies are mainly due to land clearing of the agriculture plots.
{"title":"Land Cover Change Detection around Upstream of Serayu Watershed using Machine Learning","authors":"Z. D. O. Permata, D. B. Sencaki, Afifuddin, M. Frederik, H. Priyadi, M. N. Putri, S. Arfah, Agustan, F. Alhasanah, L. Sumargana, R. Arifandri, N. Anatoly","doi":"10.1109/ISMODE56940.2022.10180994","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180994","url":null,"abstract":"Water resources are important to be managed in a sustainable manner. Changes to the land cover such as from forest to agriculture will affect the water quality and quantity of the whole watershed system. The Serayu watershed in Central Java is considered one of the critical watersheds in Indonesia due to the high erosion and sedimentation rate from the conversion of forested land to horticulture land. This paper presents a study of land cover change using the Sentine1-2 satellite images from 2018- 2019 to 2020-2021 using the machine learning method. The Sentine1-2 images have a temporal resolution of 10 days which is necessary because of the high cloud cover in the study area. Image classification using the Light Gradient Boosting yields an overall accuracy from the training and testing dataset of 1.0 and 0.929 for images 2018 – 2019 and 1.0 and 0.915 for images 2020 – 2021. Field verification upstream of the Serayu watershed shows a good agreement with the classification results, where discrepancies are mainly due to land clearing of the agriculture plots.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144470","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180924
Edi Johan Syah Djula, Rahadian Yusuf
The Intelligent Transportation System (ITS) is a part of the application of computer vision to transportation systems, which is nothing more than a form of integration between information systems, telecommunication and transportation infrastructure, vehicles, and road users. As a result, ITS can not only solve traffic problems, but also reduce the use of private vehicles and increase the efficiency of public transportation by the community if road users’ comfort and safety continues to improve. The implementation of ITS in several developed countries serves as a model for its achievements. In this study, YOLOv7 was used to classify vehicles using CCTV data from ATCS Bandung City. Taking a number of data to obtain enough data for further separation of data from the CCTV image capture into parts of the vehicle class. A pretraining model is used to identify the target vehicle based on this classification. This data processing allows for the prediction and calculation of road loads, which have long been a source of traffic congestion in Bandung, particularly in the Dago area.
{"title":"Vehicle Detection with YOLOv7 on Study Case Public Transportation and General Classification, Prediction of Road Loads","authors":"Edi Johan Syah Djula, Rahadian Yusuf","doi":"10.1109/ISMODE56940.2022.10180924","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180924","url":null,"abstract":"The Intelligent Transportation System (ITS) is a part of the application of computer vision to transportation systems, which is nothing more than a form of integration between information systems, telecommunication and transportation infrastructure, vehicles, and road users. As a result, ITS can not only solve traffic problems, but also reduce the use of private vehicles and increase the efficiency of public transportation by the community if road users’ comfort and safety continues to improve. The implementation of ITS in several developed countries serves as a model for its achievements. In this study, YOLOv7 was used to classify vehicles using CCTV data from ATCS Bandung City. Taking a number of data to obtain enough data for further separation of data from the CCTV image capture into parts of the vehicle class. A pretraining model is used to identify the target vehicle based on this classification. This data processing allows for the prediction and calculation of road loads, which have long been a source of traffic congestion in Bandung, particularly in the Dago area.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128338518","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180969
Yuri Pamungkas, A. Wibawa, Yahya Rais
Affective computing research related to EEG-based emotion recognition has become a current research trend. This research becomes very interesting because the EEG signal is complex and always changes depending on the condition of the individual at that time. So, if the information in the EEG signal can be extracted, a person’s emotional state (which tends to be hidden) will be revealed. Therefore, this study directly proposes an automatic emotion recognition system with recorded EEG data. In this study, EEG recording was performed on 32 participants. Raw EEG data is processed by stages such as pre-processing, subband decomposition, feature extraction, and classification of emotions based on feature values. The EEG signal features explored include mean value, MAV, standard deviation, variance, skewness, kurtosis, zerocrossing rate, and median. Based on the results of EEG feature extraction, it can be seen that positive-negative emotions have different feature values and the differences are also significant. The results of signal feature extraction are presented based on channels (FP1, FP2, F7, and F8) and EEG subbands (Alpha, Beta, and Gamma) for each emotional state (positive-negative). In addition, the best accuracy values for emotion classification are 93.75% (RNN), 93.75% (LSTM), and 92.97% (Bi-LSTM) in the classifier testing process.
{"title":"Classification of Emotions (Positive-Negative) Based on EEG Statistical Features using RNN, LSTM, and Bi-LSTM Algorithms","authors":"Yuri Pamungkas, A. Wibawa, Yahya Rais","doi":"10.1109/ISMODE56940.2022.10180969","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180969","url":null,"abstract":"Affective computing research related to EEG-based emotion recognition has become a current research trend. This research becomes very interesting because the EEG signal is complex and always changes depending on the condition of the individual at that time. So, if the information in the EEG signal can be extracted, a person’s emotional state (which tends to be hidden) will be revealed. Therefore, this study directly proposes an automatic emotion recognition system with recorded EEG data. In this study, EEG recording was performed on 32 participants. Raw EEG data is processed by stages such as pre-processing, subband decomposition, feature extraction, and classification of emotions based on feature values. The EEG signal features explored include mean value, MAV, standard deviation, variance, skewness, kurtosis, zerocrossing rate, and median. Based on the results of EEG feature extraction, it can be seen that positive-negative emotions have different feature values and the differences are also significant. The results of signal feature extraction are presented based on channels (FP1, FP2, F7, and F8) and EEG subbands (Alpha, Beta, and Gamma) for each emotional state (positive-negative). In addition, the best accuracy values for emotion classification are 93.75% (RNN), 93.75% (LSTM), and 92.97% (Bi-LSTM) in the classifier testing process.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717993","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180928
Rohan Mitra, D. Varam, Eyad Ali, Hana Sulieman, Firuz Kamalov
The primary objective of this paper is to present a set of synthetically generated datasets as a benchmark for evaluating feature selection algorithms (FSAs). The use of synthetic datasets is encouraged because of their utility in controlling data parameters, including the exact number of relevant, redundant, and irrelevant features. This paper proposes four numeric datasets with several sources of inspiration, namely based on geometric objects, trigonometric equations and multi-cut linear combinations. These synthetically generated datasets come with a fixed number of relevant, redundant and irrelevant features, which are then evaluated using feature selection algorithms currently popular within industry and academia. This highlights the function of these datasets as benchmarks for future researchers in the field of feature selection. Accordingly, the datasets will also be made available through GitHub for use as evaluation metrics, whilst the code is made available to be modified according to the application for the researcher. This may include research into the performance of FSAs, the development of new synthetic data, and beyond.
{"title":"Development of Synthetic Data Benchmarks for Evaluating Feature Selection Algorithms","authors":"Rohan Mitra, D. Varam, Eyad Ali, Hana Sulieman, Firuz Kamalov","doi":"10.1109/ISMODE56940.2022.10180928","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180928","url":null,"abstract":"The primary objective of this paper is to present a set of synthetically generated datasets as a benchmark for evaluating feature selection algorithms (FSAs). The use of synthetic datasets is encouraged because of their utility in controlling data parameters, including the exact number of relevant, redundant, and irrelevant features. This paper proposes four numeric datasets with several sources of inspiration, namely based on geometric objects, trigonometric equations and multi-cut linear combinations. These synthetically generated datasets come with a fixed number of relevant, redundant and irrelevant features, which are then evaluated using feature selection algorithms currently popular within industry and academia. This highlights the function of these datasets as benchmarks for future researchers in the field of feature selection. Accordingly, the datasets will also be made available through GitHub for use as evaluation metrics, whilst the code is made available to be modified according to the application for the researcher. This may include research into the performance of FSAs, the development of new synthetic data, and beyond.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129902195","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180942
I. Usman, Z. Zainuddin, S. Syarif
This study intends to assess the activity of user behavior toward sensors in the use of electronic equipment, which results in a level of wastage of electrical equipment as a baseline for the construction of wasteful and non-wasteful linguistics. At present, saving electrical energy is a prominent study area. Furthermore, it is barely discussed regarding technology to determine user behavior and the level of waste in a single device in order to maximize effectiveness and save electricity for people’s lives. The primary objective of this study’s IoT-based SmartHome model is to control the output of the Current sensor and voltage sensor installed in each Circuit Unit alongside Electrical Equipment by giving the data to a Spreadsheet to be processed with program procedures in NodewMCU-ESP8266 as a WiFi-based delivery device. It did cause the microcontroller to react to the input current sensor and voltage sensor to output potential control data for wasteful or non-wasteful conditions applied to lights (room, living room, terrace), fans, air conditioners (AC), water pumps, refrigerators, rice cookers, televisions, and used to monitor waste levels using Ultrasonic sensors, Lumens, temperature, infrared, and other sensors using the C.4.5 Algorithm Method in the house. This technology is functional and used whether the user is inside or outside the house. Currently, the results of the tool in the box packaging and data testing results reveal that the proposed Smart Home model can perform properly and successfully according to the design.
{"title":"Monitoring System Behavior in The Use of Electrical Equipment IOT-Based Smart Home","authors":"I. Usman, Z. Zainuddin, S. Syarif","doi":"10.1109/ISMODE56940.2022.10180942","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180942","url":null,"abstract":"This study intends to assess the activity of user behavior toward sensors in the use of electronic equipment, which results in a level of wastage of electrical equipment as a baseline for the construction of wasteful and non-wasteful linguistics. At present, saving electrical energy is a prominent study area. Furthermore, it is barely discussed regarding technology to determine user behavior and the level of waste in a single device in order to maximize effectiveness and save electricity for people’s lives. The primary objective of this study’s IoT-based SmartHome model is to control the output of the Current sensor and voltage sensor installed in each Circuit Unit alongside Electrical Equipment by giving the data to a Spreadsheet to be processed with program procedures in NodewMCU-ESP8266 as a WiFi-based delivery device. It did cause the microcontroller to react to the input current sensor and voltage sensor to output potential control data for wasteful or non-wasteful conditions applied to lights (room, living room, terrace), fans, air conditioners (AC), water pumps, refrigerators, rice cookers, televisions, and used to monitor waste levels using Ultrasonic sensors, Lumens, temperature, infrared, and other sensors using the C.4.5 Algorithm Method in the house. This technology is functional and used whether the user is inside or outside the house. Currently, the results of the tool in the box packaging and data testing results reveal that the proposed Smart Home model can perform properly and successfully according to the design.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132086609","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180992
M. A. Murti, Andi Shridivia Nuran, M. H. Barri, Faisal Budiman, Musrinah
Electricity use can be identified through commercial electricity meters, which are generally used today, where the information provided is only total electricity usage, which is less effective in electricity management. Electricity management can be done by monitoring and knowing active electrical appliances. In addition, load identification systems can be utilized in various applications such as electricity theft monitoring systems, electricity billing systems, Etc. This study designed a smart metering system to identify household electronic appliances based on their electricity usage profile. The contribution of this research is on how to implement a sensor and microcontroller to measure several electrical parameters consumed by household appliances and embed the system with K-Nearest Neighbors (K -NN) and Decision Tree (DT) algorithm for load classification. As the main contribution, the proposed method is to implement the proposed algorithm on an ARM-based processor and only send the result data as identified load and time stamp to the Internet. This approach will reduce the data size and energy consumption of smart devices for data transmission. The system was tested to classify some household electronic appliances (i.e., fans, televisions, smartphone chargers, rice cookers, and lamps), and both methods were compared under the same regulated conditions. The results show that the system can measure the electrical parameters of electronic appliances and identify the load type, with the DT’s prediction accuracy superior to K-NN in experiments under single-load and multi-load conditions.
{"title":"Comparison of Decision Tree and K-Nearest Neighbors Methods on Classifying Household Electrical Appliances Based on Electricity Usage Profiles","authors":"M. A. Murti, Andi Shridivia Nuran, M. H. Barri, Faisal Budiman, Musrinah","doi":"10.1109/ISMODE56940.2022.10180992","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180992","url":null,"abstract":"Electricity use can be identified through commercial electricity meters, which are generally used today, where the information provided is only total electricity usage, which is less effective in electricity management. Electricity management can be done by monitoring and knowing active electrical appliances. In addition, load identification systems can be utilized in various applications such as electricity theft monitoring systems, electricity billing systems, Etc. This study designed a smart metering system to identify household electronic appliances based on their electricity usage profile. The contribution of this research is on how to implement a sensor and microcontroller to measure several electrical parameters consumed by household appliances and embed the system with K-Nearest Neighbors (K -NN) and Decision Tree (DT) algorithm for load classification. As the main contribution, the proposed method is to implement the proposed algorithm on an ARM-based processor and only send the result data as identified load and time stamp to the Internet. This approach will reduce the data size and energy consumption of smart devices for data transmission. The system was tested to classify some household electronic appliances (i.e., fans, televisions, smartphone chargers, rice cookers, and lamps), and both methods were compared under the same regulated conditions. The results show that the system can measure the electrical parameters of electronic appliances and identify the load type, with the DT’s prediction accuracy superior to K-NN in experiments under single-load and multi-load conditions.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128661443","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180967
Jonayet Miah, M. Mamun, Md Minhazur Rahman, Md Ishtyaq Mahmyd, Asm Mohaimenul Islam, Sabbir Ahmed
Mobile phones and other electronic gadgets/devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data(m-health). Mobile health data use mobile devices to gather clinical health data and track patients’ vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on m-health. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% in accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F-1 score. Our research indicated a promising future in m-health being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.
{"title":"MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning Models","authors":"Jonayet Miah, M. Mamun, Md Minhazur Rahman, Md Ishtyaq Mahmyd, Asm Mohaimenul Islam, Sabbir Ahmed","doi":"10.1109/ISMODE56940.2022.10180967","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180967","url":null,"abstract":"Mobile phones and other electronic gadgets/devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data(m-health). Mobile health data use mobile devices to gather clinical health data and track patients’ vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on m-health. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% in accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F-1 score. Our research indicated a promising future in m-health being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122795121","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180936
Ramadhana, H. Nuha, M. Fathoni
In this era of increasingly widespread innovation developments in the field of Information Technology (IT), technology is something that is implemented as a tool to facilitate human activities. In daily human activities, it is always related to the use of writing instruments for administrative needs or other needs. In the process of printing images or writing, a high-quality printer is needed to get good printouts, of course this will cause a lot of costs to be incurred. Apart from needing a high-quality printer machine, we also have to prepare the best version of ink so that the resulting prints will be better. With the advancement of technology in the Internet of Things (IoT) which is fast enough, a tool can be created to make writing or pictures automatically and can produce good and precise prints. This machine is based on the X and Y axes, to operate the machine requires a computer unit, arduino, and also Computer Numerical Control (CNC), later this machine operates according to instructions from the computer.
{"title":"Precision Analysis on Automatic Writing Machine Using Arduino (Case Study: Printer Plotter)","authors":"Ramadhana, H. Nuha, M. Fathoni","doi":"10.1109/ISMODE56940.2022.10180936","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180936","url":null,"abstract":"In this era of increasingly widespread innovation developments in the field of Information Technology (IT), technology is something that is implemented as a tool to facilitate human activities. In daily human activities, it is always related to the use of writing instruments for administrative needs or other needs. In the process of printing images or writing, a high-quality printer is needed to get good printouts, of course this will cause a lot of costs to be incurred. Apart from needing a high-quality printer machine, we also have to prepare the best version of ink so that the resulting prints will be better. With the advancement of technology in the Internet of Things (IoT) which is fast enough, a tool can be created to make writing or pictures automatically and can produce good and precise prints. This machine is based on the X and Y axes, to operate the machine requires a computer unit, arduino, and also Computer Numerical Control (CNC), later this machine operates according to instructions from the computer.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131250363","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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