Pub Date : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001785
Wan Mohammad Amirul Bin Mohd Adnan, Anees Bt Abdul Aziz, L. Raya
Wind energy is categorised as a renewable source. Wind turbines are the main medium used to convert wind energy into electrical energy. In this project, a preliminary study of a small-scale wind power generation system is investigated. The way the tool works when the wind turbine rotates is that the generator will produce electrical power, which has been connected to the charger controller before the electric power is stored in the battery. The use of electric power from the battery will be converted using an inverter to convert the DC current to AC. From this study, the amount of wind energy used to generate electricity is dependent on the number of blades, wind speed, and distance. The 12V battery is fully charged within 2 hours with the parameter of 11 blades and a wind speed of 4.4m/s. Hence, the AC voltage, AC current, and power of appliances are calculated and can be accessed through the Blynk application on a smartphone.
{"title":"Feasibility Study of Wind Power Generation System Using Small Scale Wind Turbines","authors":"Wan Mohammad Amirul Bin Mohd Adnan, Anees Bt Abdul Aziz, L. Raya","doi":"10.1109/ICSPC55597.2022.10001785","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001785","url":null,"abstract":"Wind energy is categorised as a renewable source. Wind turbines are the main medium used to convert wind energy into electrical energy. In this project, a preliminary study of a small-scale wind power generation system is investigated. The way the tool works when the wind turbine rotates is that the generator will produce electrical power, which has been connected to the charger controller before the electric power is stored in the battery. The use of electric power from the battery will be converted using an inverter to convert the DC current to AC. From this study, the amount of wind energy used to generate electricity is dependent on the number of blades, wind speed, and distance. The 12V battery is fully charged within 2 hours with the parameter of 11 blades and a wind speed of 4.4m/s. Hence, the AC voltage, AC current, and power of appliances are calculated and can be accessed through the Blynk application on a smartphone.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"44 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120887928","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001815
Zurida Ishak, R. A. Abbas, Vitiyataran A-L T M Karunanithi
The motivation of this research is to inspect the factors and the impact of adaptation for collaboration tools and introduce a model that facilitates the process of selecting the right collaboration tools based on the challenges the team is trying to bridge in private Information Technology (IT) firms. To achieve the objectives of this study, the researchers used a descriptive-analytical method by developing a questionnaire and data collection from the survey. A comprehensive survey was adopted, and the participants in the study were selected employees from private IT companies in Johor Bahru, Malaysia, who were randomly selected using a convenience sampling technique. Both sample selection and survey distribution have been made online owing to a strict Movement Control Order (MCO). This study addresses and supports H1, H2, and H5. Therefore, communication and task coordination are the factors facilitating the distributed project teams to adopt and use various collaboration tools. In addition, task characteristics might change the use of different or multiple tools for communication. This research is based on the analysis of collaboration tools, and the results are a model of the aspects influencing the distributed teams to adopt the best selection of collaboration tools in the category of communication and coordination tools.
{"title":"A Model for Collaboration Tool Adoption in Distributed Project Team","authors":"Zurida Ishak, R. A. Abbas, Vitiyataran A-L T M Karunanithi","doi":"10.1109/ICSPC55597.2022.10001815","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001815","url":null,"abstract":"The motivation of this research is to inspect the factors and the impact of adaptation for collaboration tools and introduce a model that facilitates the process of selecting the right collaboration tools based on the challenges the team is trying to bridge in private Information Technology (IT) firms. To achieve the objectives of this study, the researchers used a descriptive-analytical method by developing a questionnaire and data collection from the survey. A comprehensive survey was adopted, and the participants in the study were selected employees from private IT companies in Johor Bahru, Malaysia, who were randomly selected using a convenience sampling technique. Both sample selection and survey distribution have been made online owing to a strict Movement Control Order (MCO). This study addresses and supports H1, H2, and H5. Therefore, communication and task coordination are the factors facilitating the distributed project teams to adopt and use various collaboration tools. In addition, task characteristics might change the use of different or multiple tools for communication. This research is based on the analysis of collaboration tools, and the results are a model of the aspects influencing the distributed teams to adopt the best selection of collaboration tools in the category of communication and coordination tools.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856026","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001798
Zulkiffli Abd Kadir, Mohamad Huzairi Mohamad Yusri, Amrina Rasyada Zubir, K. Hudha, N. H. Amer, Nur Akmal Haniffah
This study investigates a yaw motion analysis of single trailer-truck using yaw amplification factor (YAF) approach. Unwanted yaw motion in single-trailer truck can cause instability of the truck vehicle and resulting road accident such as knife-jacking. This study focused on applying brake pressure to predict the jack-knife accident. The trailer truck was tested at 100 km/h while applying various brake pressures at 0.5 MPa, 1.0 MPa, 1.5 MPa, 2.0 MPa and 2.5 MPa during single lane change and double lane change using TruckSim software. Yaw Amplification Factor (YAF) was used to analyse the yaw movement in the single-trailer truck. The indicator of yaw amplification factor to predict jack-knifing which is related to unwanted yaw motion of a truck was implemented in this study to the potential accident analysis. This study showed that jack-knife accident happened when travelling at 100 km/h when applying the brake pressures of 1.5 MPa and 2.5 MPa for single lane change manoeuvre. Similarly, jack-knife accident also happened when travelling at 100 km/h for double lane change manoeuvre with brake pressures of 1.5 MPa, 2.0 MPa and 2.5 MPa. These findings are based on Cm indicator to predict jack-knife accident.
{"title":"Yaw Motion Analysis of Single-Trailer Truck Using Yaw Amplification Factor (YAF)","authors":"Zulkiffli Abd Kadir, Mohamad Huzairi Mohamad Yusri, Amrina Rasyada Zubir, K. Hudha, N. H. Amer, Nur Akmal Haniffah","doi":"10.1109/ICSPC55597.2022.10001798","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001798","url":null,"abstract":"This study investigates a yaw motion analysis of single trailer-truck using yaw amplification factor (YAF) approach. Unwanted yaw motion in single-trailer truck can cause instability of the truck vehicle and resulting road accident such as knife-jacking. This study focused on applying brake pressure to predict the jack-knife accident. The trailer truck was tested at 100 km/h while applying various brake pressures at 0.5 MPa, 1.0 MPa, 1.5 MPa, 2.0 MPa and 2.5 MPa during single lane change and double lane change using TruckSim software. Yaw Amplification Factor (YAF) was used to analyse the yaw movement in the single-trailer truck. The indicator of yaw amplification factor to predict jack-knifing which is related to unwanted yaw motion of a truck was implemented in this study to the potential accident analysis. This study showed that jack-knife accident happened when travelling at 100 km/h when applying the brake pressures of 1.5 MPa and 2.5 MPa for single lane change manoeuvre. Similarly, jack-knife accident also happened when travelling at 100 km/h for double lane change manoeuvre with brake pressures of 1.5 MPa, 2.0 MPa and 2.5 MPa. These findings are based on Cm indicator to predict jack-knife accident.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"41 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104256","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001821
Myrtel S. Bernardo, Arnel C. Fajardo, R. Medina
The solar-sharing smart lighting system Artificial Intelligence (AI) that automatically adjusts the LED lighting appropriate for indoor vertical farming specifically on high-valued crops tested on lettuce (Lactuca sativa). The concept of this study is to make a smart lighting system more economical using Arduino and power-efficient by using renewable energy like solar power and to address the Sustainable Development Goals regarding Food Security. This study aims to investigate the effects of light treatments on the growth performance in terms of weekly plant height under varying lighting control. Hence, smart LED lighting will be developed to automatically adjust the light intensity needed by the plants to grow indoor using a hydroponics system by means of Arduino and using the Internet of Things (IoT). The experimental setup consists of three treatments: (A) smart lighting, (B) smart lighting with manual setting, and (C) smart lighting with IoT settings. Additionally, it offers the light required for photosynthesis at various phases of growth. The entire system is controlled by a microcontroller, which decides when to turn on and off the LEDs and adjusts their brightness in accordance with the amount of sunlight. The system was implemented successfully, and growth performance improved.
{"title":"End-Product of Solar-Sharing Smart Lighting Artificial Intelligence Driven Platform for High-Valued Crops (Lactuca Sativa) on Indoor Hydroponics Syste","authors":"Myrtel S. Bernardo, Arnel C. Fajardo, R. Medina","doi":"10.1109/ICSPC55597.2022.10001821","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001821","url":null,"abstract":"The solar-sharing smart lighting system Artificial Intelligence (AI) that automatically adjusts the LED lighting appropriate for indoor vertical farming specifically on high-valued crops tested on lettuce (Lactuca sativa). The concept of this study is to make a smart lighting system more economical using Arduino and power-efficient by using renewable energy like solar power and to address the Sustainable Development Goals regarding Food Security. This study aims to investigate the effects of light treatments on the growth performance in terms of weekly plant height under varying lighting control. Hence, smart LED lighting will be developed to automatically adjust the light intensity needed by the plants to grow indoor using a hydroponics system by means of Arduino and using the Internet of Things (IoT). The experimental setup consists of three treatments: (A) smart lighting, (B) smart lighting with manual setting, and (C) smart lighting with IoT settings. Additionally, it offers the light required for photosynthesis at various phases of growth. The entire system is controlled by a microcontroller, which decides when to turn on and off the LEDs and adjusts their brightness in accordance with the amount of sunlight. The system was implemented successfully, and growth performance improved.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134126441","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 main functions of automated systems rely on advanced sensors for the detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in extreme weather conditions, such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in the vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, an innovative triple radar arrangement (long-range, medium-range, and short-range radars) with a sensor fusion technique is proposed to detect objects of different sizes in the level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including trucks, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and the inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with a triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.
{"title":"A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles","authors":"Javad Enayati, Yeshwanth Jonnalagadda, Pedram Asef","doi":"10.1109/ICSPC55597.2022.10001787","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001787","url":null,"abstract":"The main functions of automated systems rely on advanced sensors for the detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in extreme weather conditions, such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in the vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, an innovative triple radar arrangement (long-range, medium-range, and short-range radars) with a sensor fusion technique is proposed to detect objects of different sizes in the level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including trucks, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and the inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with a triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134214575","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001769
Safaa N. Saud Al-Humairi, Khisharn A-L Selvamani, L. Raya
Home security is a vital element of home computerization and perhaps the most significant matter in the current era of technology. This paper presents a conceptual design and development of an IoT contactless door buzzer, automation and home security device using a Raspberry Pi microprocessor and mechanical system embedded with a home security camera. Therefore, the main objective of our work is to design a system which can alert the user and others of an intruder break-in by sending a notification to their smartphones. The owner will also have the ability to stop or start the alarm remotely using just his smartphone. The proposed system aims to assist blind and visually impaired people in unlocking their house’s main door without using a manual key but by scanning the person's face through face recognition technology. A pi camera is connected directly to the Raspberry Pi. A relay controls the circuit to switch high current, on/off and electrically isolated signals, while the ultrasonic sensor is object detection in front of the door. A mobile application is developed for the user to control the device. An alert will be sent to the user if a thief breaks into the door without the owner’s consent for them to take necessary precautionary measures.
{"title":"Design and Development of an IoT Contactless Door Buzzer, Automation and Home Security Device","authors":"Safaa N. Saud Al-Humairi, Khisharn A-L Selvamani, L. Raya","doi":"10.1109/ICSPC55597.2022.10001769","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001769","url":null,"abstract":"Home security is a vital element of home computerization and perhaps the most significant matter in the current era of technology. This paper presents a conceptual design and development of an IoT contactless door buzzer, automation and home security device using a Raspberry Pi microprocessor and mechanical system embedded with a home security camera. Therefore, the main objective of our work is to design a system which can alert the user and others of an intruder break-in by sending a notification to their smartphones. The owner will also have the ability to stop or start the alarm remotely using just his smartphone. The proposed system aims to assist blind and visually impaired people in unlocking their house’s main door without using a manual key but by scanning the person's face through face recognition technology. A pi camera is connected directly to the Raspberry Pi. A relay controls the circuit to switch high current, on/off and electrically isolated signals, while the ultrasonic sensor is object detection in front of the door. A mobile application is developed for the user to control the device. An alert will be sent to the user if a thief breaks into the door without the owner’s consent for them to take necessary precautionary measures.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124528887","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001786
Kavindran Suresh, M. R. Ghazali, Mohd Ashraf Ahmad
The detection of cupped suction system plants using a standard model based on a modified Sine Cosine Algorithm (mSCA) is presented in this research. According to the findings, the mSCA-based technique can produce optimal parameters of model that provides an identified output response comparable to the actual experiment's cupping suction system output, with an integral square error for various random input surfaces and its objective function. The input and output data were used to create this modelling output variable of the cupping suction system detected by connecting the differential pressure sensor to the cup. In contrast, the input variable is determined by the speed of the pump applied in various locations. The transfer function model also makes use of the continuous-time transfer function.
{"title":"Modelling of Cupping Suction System based on System Identification Method","authors":"Kavindran Suresh, M. R. Ghazali, Mohd Ashraf Ahmad","doi":"10.1109/ICSPC55597.2022.10001786","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001786","url":null,"abstract":"The detection of cupped suction system plants using a standard model based on a modified Sine Cosine Algorithm (mSCA) is presented in this research. According to the findings, the mSCA-based technique can produce optimal parameters of model that provides an identified output response comparable to the actual experiment's cupping suction system output, with an integral square error for various random input surfaces and its objective function. The input and output data were used to create this modelling output variable of the cupping suction system detected by connecting the differential pressure sensor to the cup. In contrast, the input variable is determined by the speed of the pump applied in various locations. The transfer function model also makes use of the continuous-time transfer function.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115017071","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001742
Alimul Mahfuz Tushar, A. Wazed, Ehsanuzzaman Shawon, Muntasir Rahman, Md. Ismail Hossen, M. Z. H. Jesmeen
Last couple of years a lot of people are dying because of heart related disease and now this is one of the most concerning and life-threatening disease all over the world. It is also a concerning matter for health industry. About one person dies from heart disease every minute in the modern era. As heart disease prediction is a critical task, there is a need to automate the prediction process to avoid risks associated with it and inform the patient in advance. So, there is need a system or technique to diagnose this disease with maximum accuracy. Machine learning algorithm and technique can be helpful for health care industry because it has the ability to analyze large and complex data set. In this paper, we will exhibit how to utilize various kinds of machine learning models likes Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF), Logistic Regression and predicts the chances of heart disease and classifies patient's risk.
在过去的几年里，很多人死于心脏相关疾病，现在这是世界上最令人担忧和威胁生命的疾病之一。这也是卫生行业关注的问题。在现代社会，每分钟大约有一人死于心脏病。由于心脏病预测是一项关键任务，因此有必要将预测过程自动化，以避免与之相关的风险，并提前通知患者。因此，需要一种系统或技术来最大限度地准确诊断这种疾病。机器学习算法和技术可以帮助医疗保健行业，因为它具有分析大型复杂数据集的能力。在本文中，我们将展示如何利用各种机器学习模型，如支持向量机(SVM)， k -最近邻(KNN)， Naïve贝叶斯，决策树(DT)，随机森林(RF)，逻辑回归和预测心脏病的机会并对患者的风险进行分类。
{"title":"A Review of Commonly used Machine Learning Classifiers in Heart Disease Prediction","authors":"Alimul Mahfuz Tushar, A. Wazed, Ehsanuzzaman Shawon, Muntasir Rahman, Md. Ismail Hossen, M. Z. H. Jesmeen","doi":"10.1109/ICSPC55597.2022.10001742","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001742","url":null,"abstract":"Last couple of years a lot of people are dying because of heart related disease and now this is one of the most concerning and life-threatening disease all over the world. It is also a concerning matter for health industry. About one person dies from heart disease every minute in the modern era. As heart disease prediction is a critical task, there is a need to automate the prediction process to avoid risks associated with it and inform the patient in advance. So, there is need a system or technique to diagnose this disease with maximum accuracy. Machine learning algorithm and technique can be helpful for health care industry because it has the ability to analyze large and complex data set. In this paper, we will exhibit how to utilize various kinds of machine learning models likes Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF), Logistic Regression and predicts the chances of heart disease and classifies patient's risk.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117297971","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001768
Awangku Harraz Aiman Awangku Bolkiah, Hafizatul Hanin Hamzah, Z. Ibrahim, N. Diah, Azizian Mohd Sapawi, H. M. Hanum
An increasing crime rate among urban residents has become a major concern over the last decade. Prevention can be done with advanced prediction based on criminal activities and locations. A publicly available dataset from kaggle.com was used in this research, consisting of 500 records of information, such as the coordinates of the crime locations and the types of crimes. An unsupervised machine learning algorithm, K-Means Clustering, is applied to group the data based on the locations of the reported crimes. Then, Support Vector Machine, a supervised machine learning algorithm, is applied to predict the potential crime locations. Thus, law enforcement agencies can make strategic plans and deploy their units to the predicted crime scenes, decreasing the chances of crimes being committed. Even though the integration of K-Means Clustering and Support Vector Machine for crime scene prediction only shows 0.65 accuracies, improvements can still be made for future work with larger datasets and integrating other machine learning algorithms.
{"title":"Crime Scene Prediction Using the Integration of K-Means Clustering and Support Vector Machine","authors":"Awangku Harraz Aiman Awangku Bolkiah, Hafizatul Hanin Hamzah, Z. Ibrahim, N. Diah, Azizian Mohd Sapawi, H. M. Hanum","doi":"10.1109/ICSPC55597.2022.10001768","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001768","url":null,"abstract":"An increasing crime rate among urban residents has become a major concern over the last decade. Prevention can be done with advanced prediction based on criminal activities and locations. A publicly available dataset from kaggle.com was used in this research, consisting of 500 records of information, such as the coordinates of the crime locations and the types of crimes. An unsupervised machine learning algorithm, K-Means Clustering, is applied to group the data based on the locations of the reported crimes. Then, Support Vector Machine, a supervised machine learning algorithm, is applied to predict the potential crime locations. Thus, law enforcement agencies can make strategic plans and deploy their units to the predicted crime scenes, decreasing the chances of crimes being committed. Even though the integration of K-Means Clustering and Support Vector Machine for crime scene prediction only shows 0.65 accuracies, improvements can still be made for future work with larger datasets and integrating other machine learning algorithms.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125908395","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 : 2022-12-17DOI: 10.1109/ICSPC55597.2022.10001823
Z. Zyada, Khaled Alharbi, Mohamed A. Omar
Robotics based rehabilitation have attracted much attention during the past two decades. Recent intensive research publications in the area indicates that soft exoskeleton is one of the promising technologies for robotic-based rehabilitation. This paper aims to review the up-to-date research work in soft exoskeletons material, manufacturing, sensing and control for hand rehabilitation. Applied materials, its preferred properties, and manufacturing technologies of soft exoskeletons are reviewed. Different position and force sensing technologies as well as recent control techniques applying bio-signals as control signals are reviewed. The major challenges, which are also recommendations for future research work, are highlighted.
{"title":"Soft Exoskeleton for Hand Rehabilitation: An Overview","authors":"Z. Zyada, Khaled Alharbi, Mohamed A. Omar","doi":"10.1109/ICSPC55597.2022.10001823","DOIUrl":"https://doi.org/10.1109/ICSPC55597.2022.10001823","url":null,"abstract":"Robotics based rehabilitation have attracted much attention during the past two decades. Recent intensive research publications in the area indicates that soft exoskeleton is one of the promising technologies for robotic-based rehabilitation. This paper aims to review the up-to-date research work in soft exoskeletons material, manufacturing, sensing and control for hand rehabilitation. Applied materials, its preferred properties, and manufacturing technologies of soft exoskeletons are reviewed. Different position and force sensing technologies as well as recent control techniques applying bio-signals as control signals are reviewed. The major challenges, which are also recommendations for future research work, are highlighted.","PeriodicalId":334831,"journal":{"name":"2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129397816","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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