S. Marjudi, Roziyani Setik, Mohamad Aizi Salamat, Muhammad Fahruddin Irfan Yusfaidir
Agriculture is on the verge of entering the Smart Farming era, in which farming operations will become digitalized and data-driven, allowing for better decision support, smart analytics, and forecasting. Farming is the most diverse economic sector and is critical to a country's overall economic development. The Internet of Things (IoT) can potentially optimize agriculture and farming sector activities by reducing manpower through technology. Forecasts are central to most agricultural and agricultural-related operations. Smart Agriculture Data Analytics (SADA) was developed to address this issue. SADA is an embedded system with two components: data analytics and the Internet of Things (IoT). IoT in SADA also assists farmers in collecting data and learning more about the appropriate soil PH scale, fertilizer dataset, air humidity, and temperature. A prototyping model is used in software development. The farmer can provide real-time feedback, request project changes, and update model specifications. SADA will help farmers understand the trend of analytics crop production, allowing them to increase yield
{"title":"The Design and Development of Smart Agriculture Data Analytics","authors":"S. Marjudi, Roziyani Setik, Mohamad Aizi Salamat, Muhammad Fahruddin Irfan Yusfaidir","doi":"10.46338/ijetae0123_10","DOIUrl":"https://doi.org/10.46338/ijetae0123_10","url":null,"abstract":"Agriculture is on the verge of entering the Smart Farming era, in which farming operations will become digitalized and data-driven, allowing for better decision support, smart analytics, and forecasting. Farming is the most diverse economic sector and is critical to a country's overall economic development. The Internet of Things (IoT) can potentially optimize agriculture and farming sector activities by reducing manpower through technology. Forecasts are central to most agricultural and agricultural-related operations. Smart Agriculture Data Analytics (SADA) was developed to address this issue. SADA is an embedded system with two components: data analytics and the Internet of Things (IoT). IoT in SADA also assists farmers in collecting data and learning more about the appropriate soil PH scale, fertilizer dataset, air humidity, and temperature. A prototyping model is used in software development. The farmer can provide real-time feedback, request project changes, and update model specifications. SADA will help farmers understand the trend of analytics crop production, allowing them to increase yield","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125455639","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 process of transmitting power from one node to another via physical links without using wires is denoted as wireless power transfer (WPT). WPT plays an important role in medical applications for it offers the possibility of avoiding surgical operations during the replacement of embedded batteries in human body. In this paper, a WPT system for transferring power and DATA to deeply embedded biosensors is proposed. The system is designed such that it is capable of powering a biosensor simultaneously with DATA transmission from a transmitting coil located outside the human body at a distance of 100mm from the biosensor node. The simulation results have revealed the reception of a regulated DC voltage at the biosensor node of 1.2V, which is sufficient to charge a rechargeable embedded battery with an average DC current of 1.1mA. The results also reveal that the proposed system has succeeded in the extraction of the transmitted DATA at the biosensor side within a time of 30µs. The transmitter of the proposed WPT system is driven by a Class-E power source, which has accomplished a drain efficiency of 72% at an overall operating power of 6W. The proposed system is designed and verified on PSpice.
{"title":"Wireless Power and DATA Transfer to Deeply-Embedded Biosensors in Human Body","authors":"Abdulkareem Mokif Obais","doi":"10.46338/ijetae1222_17","DOIUrl":"https://doi.org/10.46338/ijetae1222_17","url":null,"abstract":"The process of transmitting power from one node to another via physical links without using wires is denoted as wireless power transfer (WPT). WPT plays an important role in medical applications for it offers the possibility of avoiding surgical operations during the replacement of embedded batteries in human body. In this paper, a WPT system for transferring power and DATA to deeply embedded biosensors is proposed. The system is designed such that it is capable of powering a biosensor simultaneously with DATA transmission from a transmitting coil located outside the human body at a distance of 100mm from the biosensor node. The simulation results have revealed the reception of a regulated DC voltage at the biosensor node of 1.2V, which is sufficient to charge a rechargeable embedded battery with an average DC current of 1.1mA. The results also reveal that the proposed system has succeeded in the extraction of the transmitted DATA at the biosensor side within a time of 30µs. The transmitter of the proposed WPT system is driven by a Class-E power source, which has accomplished a drain efficiency of 72% at an overall operating power of 6W. The proposed system is designed and verified on PSpice.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124721824","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}
Azriyenni Azhari Zakri, Arfianti Arfianti, A. Hamzah, M. Iqbal, Hamdy Madjid, Naufal Fikri Aulia
Stroke patients often have trouble in daily interactions, when the patient communicated with people who are guarding them. If the distance between the patient and their guard is far, this will make it difficult for the stroke patient to communicate. Therefore, this research designed a prototype glove with a flex sensor installed as a communication tool to aid stroke patients. The designed glove is paired with five flexible sensors to enable nurses easily to read the five-finger movement signals. This tool is also equipped with DS18B20 temperature and pulse sensors capable of monitoring the physical condition of stroke patients in real-time. Testing the flex sensor glove prototype was carried out by measuring temperature & heart rate through pulse and temperature sensors. The output data is in the form of text and sound displayed on the LCD and heard through the speaker through the DFPlayer mini module. The body temperature was measured using the DS18B20 temperature sensor and compared with an Avico digital thermometer which has an average error of 0.1%, indicating adequacy. The heart rate test results through the pulse sensor were compared with measurements obtained using measure heart rate correctly. Instant heart rate, which has an average error of 0.7%, hence, it can
{"title":"Designing Flex Sensor Gloves with Temperature Sensor & Pulse Sensor to Help Stroke Patients","authors":"Azriyenni Azhari Zakri, Arfianti Arfianti, A. Hamzah, M. Iqbal, Hamdy Madjid, Naufal Fikri Aulia","doi":"10.46338/ijetae1222_03","DOIUrl":"https://doi.org/10.46338/ijetae1222_03","url":null,"abstract":"Stroke patients often have trouble in daily interactions, when the patient communicated with people who are guarding them. If the distance between the patient and their guard is far, this will make it difficult for the stroke patient to communicate. Therefore, this research designed a prototype glove with a flex sensor installed as a communication tool to aid stroke patients. The designed glove is paired with five flexible sensors to enable nurses easily to read the five-finger movement signals. This tool is also equipped with DS18B20 temperature and pulse sensors capable of monitoring the physical condition of stroke patients in real-time. Testing the flex sensor glove prototype was carried out by measuring temperature & heart rate through pulse and temperature sensors. The output data is in the form of text and sound displayed on the LCD and heard through the speaker through the DFPlayer mini module. The body temperature was measured using the DS18B20 temperature sensor and compared with an Avico digital thermometer which has an average error of 0.1%, indicating adequacy. The heart rate test results through the pulse sensor were compared with measurements obtained using measure heart rate correctly. Instant heart rate, which has an average error of 0.7%, hence, it can","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129599908","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}
Omar Outemsaa, O. E. Farissi, Lahcen Hamouti, Mohammed Modar
To minimise stresses on the tool and workpiece, such as wear, thermal effect, workpiece stresses, cutting power, etc., the cutting force and the heat in the cutting area should be minimised. This work aims to introduce an artificial intelligence tool, more precisely the neural network, to achieve optimized cutting conditions. Oxley cutting modelling in conjunction with Johnson-Cook of an AISI 1045 material is converted to an artificial neural network model which will be used to determine a fitness function to be optimized. The Artificial Neural Network is constructed based on the training data collected from the predictive model of Oxley and JC, the choice of the most accurate ANN of minimal MSE= 0.001108 is based on a specific method of tuning the hyperparameters which result in an architecture; two hidden layers, 25 neurons for each hidden layer, a sigmoid activation function, a trainlm learning algorithm, and a learning rate of 0.01. A multi-objective optimization is performed using the MATLAB tool to obtain the optimum values for cutting velocity Vc, advance f, penetration depth ap, and cutting angle of the tool. It is found that the neural network is a more rapid calculation of cutting conditions such as shear zone forces, shear zone temperatures, and others. contrary to the Oxley and JC mathematical model which will require a lot of calculations. The optimum values for cutting conditions are 208 mm/min for cutting speed, 0.06mm/rev for f, 0.38 for ap, and 10° for clearance angle.
{"title":"Cutting Forces and Temperature Optimization in Turning using a Predictive Machining Theory, ANN, and MOGA","authors":"Omar Outemsaa, O. E. Farissi, Lahcen Hamouti, Mohammed Modar","doi":"10.46338/ijetae1222_06","DOIUrl":"https://doi.org/10.46338/ijetae1222_06","url":null,"abstract":"To minimise stresses on the tool and workpiece, such as wear, thermal effect, workpiece stresses, cutting power, etc., the cutting force and the heat in the cutting area should be minimised. This work aims to introduce an artificial intelligence tool, more precisely the neural network, to achieve optimized cutting conditions. Oxley cutting modelling in conjunction with Johnson-Cook of an AISI 1045 material is converted to an artificial neural network model which will be used to determine a fitness function to be optimized. The Artificial Neural Network is constructed based on the training data collected from the predictive model of Oxley and JC, the choice of the most accurate ANN of minimal MSE= 0.001108 is based on a specific method of tuning the hyperparameters which result in an architecture; two hidden layers, 25 neurons for each hidden layer, a sigmoid activation function, a trainlm learning algorithm, and a learning rate of 0.01. A multi-objective optimization is performed using the MATLAB tool to obtain the optimum values for cutting velocity Vc, advance f, penetration depth ap, and cutting angle of the tool. It is found that the neural network is a more rapid calculation of cutting conditions such as shear zone forces, shear zone temperatures, and others. contrary to the Oxley and JC mathematical model which will require a lot of calculations. The optimum values for cutting conditions are 208 mm/min for cutting speed, 0.06mm/rev for f, 0.38 for ap, and 10° for clearance angle.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423668","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}
Stanley S. Akende, M. Ahaneku, U. Nwawelu, Uchenna C. Amazue, Douglas A. Amoke
- Relevance of the field of wireless sensor networks (WSNs) is increasing, and one of the most pressing challenges is in energy usage. This makes it a resource restraint type network for wireless sensor nodes that contain small unchangeable battery. Sensor network design has been influenced by and depends on the application by factors such as scalability, power consumption, environment etc. Most of the energy is used for communications among the three energy-saving activities: sensing, processing and communication. In this paper, an energy efficient (energy conserving) routing protocol called Wireless Sensor Network Energy Reduction Routing Coordinate Algorithm (WSNERRCA) is proposed. This provides a more efficient energy consumption pattern in WSN, by using eight straight line routing coordinate to sink. It transmits data within nodes transmission range (single-hop) and multi-hopping along routes (coordinates) thereby saving energy and optimizing delivery. The energy-model is simulated using NS-2 and the residual energy computed with the aid of AWK programming language coding. This model out-performed its counterpart (EEEWSNMIA) by 6%, as seen in recent research work published by Elsevier based on the criteria of conserving the highest energy of the sensor network with a hundred and twenty nodes while upholding optimally the QoS factors.
{"title":"Improving Energy Efficiency of Wireless Sensor Networks through Topology Optimization","authors":"Stanley S. Akende, M. Ahaneku, U. Nwawelu, Uchenna C. Amazue, Douglas A. Amoke","doi":"10.46338/ijetae1222_12","DOIUrl":"https://doi.org/10.46338/ijetae1222_12","url":null,"abstract":"- Relevance of the field of wireless sensor networks (WSNs) is increasing, and one of the most pressing challenges is in energy usage. This makes it a resource restraint type network for wireless sensor nodes that contain small unchangeable battery. Sensor network design has been influenced by and depends on the application by factors such as scalability, power consumption, environment etc. Most of the energy is used for communications among the three energy-saving activities: sensing, processing and communication. In this paper, an energy efficient (energy conserving) routing protocol called Wireless Sensor Network Energy Reduction Routing Coordinate Algorithm (WSNERRCA) is proposed. This provides a more efficient energy consumption pattern in WSN, by using eight straight line routing coordinate to sink. It transmits data within nodes transmission range (single-hop) and multi-hopping along routes (coordinates) thereby saving energy and optimizing delivery. The energy-model is simulated using NS-2 and the residual energy computed with the aid of AWK programming language coding. This model out-performed its counterpart (EEEWSNMIA) by 6%, as seen in recent research work published by Elsevier based on the criteria of conserving the highest energy of the sensor network with a hundred and twenty nodes while upholding optimally the QoS factors.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128310147","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}
Industry 4.0 brings transformation to all industries, including agriculture. Smart livestock has been replacing traditional livestock as a trend of the agricultural industry in the world. Precision feeding is one of the areas of smart husbandry that combines many modern multidisciplinary technologies which are prominent such as AI, IoT, Big Data, etc. To obtain that for pigs, a precision feeding system needs to be implemented. Components of the system include automatic feeders connected to a computer system to collect and process data on daily feed intake of fishes and animals, and/or from ambient sensors. Perturbations such as heat stress or sanitation issues have a significant impact on the nutritional profile of group housed pigs. However, perturbation is often detected only after it has occurred and is recognized late by the consequences left on the animal. Although the cause of perturbations might be unknown, the effect on the animal can be observed early throughout the data of voluntary feed intake. By the precision feeding system, the data are processed and analysed based on mathematical models following a two-step approach: (1) estimation of target trajectory of cumulative feed intake using linear and quadratic functions, and (2) detection of perturbations based on deviations from the target cumulative feed intake. However, implementing such a system requires huge costs and is often beyond the capabilities of farms, production households and small/medium laboratories. In this paper, we introduce an agent-based modeling approach to simulate precision feeding systems for swine, whose data can be used to early detect multiple perturbations which may have appeared. Experiments were carried out on GAMA simulation platform to demonstrate the efficiency in detecting multiple perturbations of group housed pigs, and also prove the usefulness of simulation of precision feeding systems.
{"title":"Detecting Multiple Perturbations on Swine using Data from Simulation of Precision Feeding Systems","authors":"X. Nguyen, L. Pham","doi":"10.46338/ijetae1222_15","DOIUrl":"https://doi.org/10.46338/ijetae1222_15","url":null,"abstract":"Industry 4.0 brings transformation to all industries, including agriculture. Smart livestock has been replacing traditional livestock as a trend of the agricultural industry in the world. Precision feeding is one of the areas of smart husbandry that combines many modern multidisciplinary technologies which are prominent such as AI, IoT, Big Data, etc. To obtain that for pigs, a precision feeding system needs to be implemented. Components of the system include automatic feeders connected to a computer system to collect and process data on daily feed intake of fishes and animals, and/or from ambient sensors. Perturbations such as heat stress or sanitation issues have a significant impact on the nutritional profile of group housed pigs. However, perturbation is often detected only after it has occurred and is recognized late by the consequences left on the animal. Although the cause of perturbations might be unknown, the effect on the animal can be observed early throughout the data of voluntary feed intake. By the precision feeding system, the data are processed and analysed based on mathematical models following a two-step approach: (1) estimation of target trajectory of cumulative feed intake using linear and quadratic functions, and (2) detection of perturbations based on deviations from the target cumulative feed intake. However, implementing such a system requires huge costs and is often beyond the capabilities of farms, production households and small/medium laboratories. In this paper, we introduce an agent-based modeling approach to simulate precision feeding systems for swine, whose data can be used to early detect multiple perturbations which may have appeared. Experiments were carried out on GAMA simulation platform to demonstrate the efficiency in detecting multiple perturbations of group housed pigs, and also prove the usefulness of simulation of precision feeding systems.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127662899","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}
In this article, the proposed antenna structure is designed for modern wireless communication systems. The antenna structure is consistent of twelve-unit metamaterial (MTM) unit cells. Therefore, the antenna size is miniaturized effectively to 30×40mm2 which is equivalently about 0.2λo, where λo is the free space wavelength at 2.7GHz. This is achieved by conducting the use of Hilbert shape MTM structure with T-resonator induction structure. The antenna structure is printed on a single side substrate to cover the frequency bands from 2.7GHz to 3.7GHz and 5.4GHz to 5.6GHz. Such antenna is found to provide a maximum gain of 2.2dBi at first and the second band of interest. Next, proposed antenna is found to be circularly polarized at 3.3GHz and 5.6GHz. The proposed antenna performance is simulated numerically using CST MWS software package with all design methodology that is chosen to arrive to the optimal performance. Then, the optimal antenna design is tested numerically using HFSS software package for validation. Finally, an excellent agreement is achieved between the two conducted software result
{"title":"Circularly Polarized Metamaterial Patch Antenna Circuitry for Modern Applications","authors":"Marwah Haleem, T. Elwi","doi":"10.46338/ijetae1222_05","DOIUrl":"https://doi.org/10.46338/ijetae1222_05","url":null,"abstract":"In this article, the proposed antenna structure is designed for modern wireless communication systems. The antenna structure is consistent of twelve-unit metamaterial (MTM) unit cells. Therefore, the antenna size is miniaturized effectively to 30×40mm2 which is equivalently about 0.2λo, where λo is the free space wavelength at 2.7GHz. This is achieved by conducting the use of Hilbert shape MTM structure with T-resonator induction structure. The antenna structure is printed on a single side substrate to cover the frequency bands from 2.7GHz to 3.7GHz and 5.4GHz to 5.6GHz. Such antenna is found to provide a maximum gain of 2.2dBi at first and the second band of interest. Next, proposed antenna is found to be circularly polarized at 3.3GHz and 5.6GHz. The proposed antenna performance is simulated numerically using CST MWS software package with all design methodology that is chosen to arrive to the optimal performance. Then, the optimal antenna design is tested numerically using HFSS software package for validation. Finally, an excellent agreement is achieved between the two conducted software result","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126935782","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}
Physical readiness is one factor that promotes student learning achievement. However, sitting for long periods can lead to myofascial pain syndrome, affecting undergraduate students’ learning outcomes who took lecturebased and computer-based sessions online for long periods. This study aims to develop a set of economic reminders using the Internet of Things and prediction modeling by the Machine Learning technique. The developed preventing myofascial pain syndrome automation system reminds the student to change sitting postures or get up and prevent myofascial pain syndrome. This system applies consecutively to students for two academic semesters. Further, this system applied the prediction models by four Machine Learning techniques. The evaluation results of model efficiency revealed that the model developed with Multi-Layer Perceptron Neural Network has the highest accuracy of 93.98%. The model with the second highest accuracy performance was the Support Vector Machine, k-Nearest Neighbor, and Decision Tree techniques were modeled with accuracy values of 91.77%, 91.31%, and 90.56%, respectively. Furthermore, the results showed that the preventing myofascial pain syndrome automation system promoted higher student learning outcomes than the group without the preventing myofascial pain syndrome automation system at a significance level of 0.05. The developed system with the prediction model also effectively prevents and reduces the number of students from myofascial pains. Thus, the developed system has shown that educational management focusing on the learners’ health will enhance learning effectiveness.
{"title":"Development of Preventing Myofascial Pain Syndrome Automation with Ultrasonic-based and Machine Learning","authors":"S. Nuanmeesri, L. Poomhiran","doi":"10.46338/ijetae1222_04","DOIUrl":"https://doi.org/10.46338/ijetae1222_04","url":null,"abstract":"Physical readiness is one factor that promotes student learning achievement. However, sitting for long periods can lead to myofascial pain syndrome, affecting undergraduate students’ learning outcomes who took lecturebased and computer-based sessions online for long periods. This study aims to develop a set of economic reminders using the Internet of Things and prediction modeling by the Machine Learning technique. The developed preventing myofascial pain syndrome automation system reminds the student to change sitting postures or get up and prevent myofascial pain syndrome. This system applies consecutively to students for two academic semesters. Further, this system applied the prediction models by four Machine Learning techniques. The evaluation results of model efficiency revealed that the model developed with Multi-Layer Perceptron Neural Network has the highest accuracy of 93.98%. The model with the second highest accuracy performance was the Support Vector Machine, k-Nearest Neighbor, and Decision Tree techniques were modeled with accuracy values of 91.77%, 91.31%, and 90.56%, respectively. Furthermore, the results showed that the preventing myofascial pain syndrome automation system promoted higher student learning outcomes than the group without the preventing myofascial pain syndrome automation system at a significance level of 0.05. The developed system with the prediction model also effectively prevents and reduces the number of students from myofascial pains. Thus, the developed system has shown that educational management focusing on the learners’ health will enhance learning effectiveness.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127078781","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 Flowerhorn is a type of ornamental fish that many fish hobbyists are attracted to because of the uniqueness of its head and the beauty of the color of the scales. And has a high selling value. The treatment requires equipment: An aquarium, oxygen aerator pump, water filter, water pH meter, UV lamp, and fish feed. In keeping Flowerhorn fish in the aquarium, you must pay attention to the acidity of the water (pH) and the feeding schedule according to the needs of the Flowerhorn fish age.Flowerhorn fish is one of the most popular ornamental fish. However, it is a little difficult to find superior seeds. To get superior quality Flowerhorn fish, IoT technology is needed to monitor the development of Flowerhorn fish using a Deep Learning approach, namely the Convex Hull Algorithms from OpenCV. OpenCV is a method that can detect objects using a camera, and the results of the image storage will be saved to a computer database that can process objects that have been tracked. Using this IoT and OpenCV system, you can feed fish, adjust water pH automatically, and identify all Flowerhorn fish in the aquarium using a camera. And can distinguish the color pattern and size of the forehead of the ideal Flowerhorn fish. So, Flowerhorn fish develop optimally and have a high selling value.
{"title":"Cultivation of Flowerhorn Species in Search of Superior Quality Seeds using IoT and Open CV","authors":"Heri Ngarianto, Eko Setyo Purwanto, Haikal Andrean","doi":"10.46338/ijetae1222_09","DOIUrl":"https://doi.org/10.46338/ijetae1222_09","url":null,"abstract":"The Flowerhorn is a type of ornamental fish that many fish hobbyists are attracted to because of the uniqueness of its head and the beauty of the color of the scales. And has a high selling value. The treatment requires equipment: An aquarium, oxygen aerator pump, water filter, water pH meter, UV lamp, and fish feed. In keeping Flowerhorn fish in the aquarium, you must pay attention to the acidity of the water (pH) and the feeding schedule according to the needs of the Flowerhorn fish age.Flowerhorn fish is one of the most popular ornamental fish. However, it is a little difficult to find superior seeds. To get superior quality Flowerhorn fish, IoT technology is needed to monitor the development of Flowerhorn fish using a Deep Learning approach, namely the Convex Hull Algorithms from OpenCV. OpenCV is a method that can detect objects using a camera, and the results of the image storage will be saved to a computer database that can process objects that have been tracked. Using this IoT and OpenCV system, you can feed fish, adjust water pH automatically, and identify all Flowerhorn fish in the aquarium using a camera. And can distinguish the color pattern and size of the forehead of the ideal Flowerhorn fish. So, Flowerhorn fish develop optimally and have a high selling value.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134216970","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}
Archolito V. Pahuriray, Joe D. Basanta, Jan Carlo T. Arroyo, A. P. Delima
The spread of the COVID-19 pandemic broughtsignificant changes in society. Emerging technologies like artificial intelligence and machine learning devices improved several industries, especially in academe and higher education institutions. In this study, a model to analyze and predict college students' sentiments from the Flexible Learning Experience portal was built using several supervised machine-learning techniques. Waikato Environment for Knowledge Analysis (WEKA) application was used to apply the Naive Bayes (NB), C4.5, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Additionally, a comparative analysis of different machine-learning methods was applied. The experimental results revealed that the C4.5 algorithmobtained the highest accuracy than other algorithms. The effectiveness of each algorithm was evaluated and compared using 10-fold crossvalidation (CV), taking into account the major accuracy metrics, instances that were accurately or inaccurately classified, kappa statistics, mean absolute error, and modeling time. Moreover, results show that the C4.5 algorithm outperformed other algorithms by classifying the model with 98.13% accuracy, 0.0132 mean absolute error, and 0.00 seconds of training time. Furthermore, teachers and college administrations were well accustomed to the sentiments and problems of college students and might act as a decisionsupport mechanism mainly as they deal with the new setting during this time of crisis.
新冠肺炎疫情的蔓延给社会带来了重大变化。人工智能和机器学习设备等新兴技术改善了多个行业，尤其是学术界和高等教育机构。在本研究中，使用几种监督式机器学习技术建立了一个模型来分析和预测灵活学习体验门户网站上的大学生情绪。使用Waikato Environment for Knowledge Analysis (WEKA)应用程序，应用朴素贝叶斯(NB)、C4.5、随机森林(RF)、支持向量机(SVM)和k -近邻(KNN)算法。此外，对不同的机器学习方法进行了比较分析。实验结果表明，C4.5算法比其他算法获得了最高的精度。使用10倍交叉验证(CV)评估和比较每种算法的有效性，同时考虑到主要精度指标、准确或不准确分类的实例、kappa统计量、平均绝对误差和建模时间。结果表明，C4.5算法的分类准确率为98.13%，平均绝对误差为0.0132，训练时间为0.00秒，优于其他算法。此外，教师和学院管理人员对大学生的情绪和问题非常熟悉，可能主要在危机时期应对新环境时发挥决策支持机制的作用。
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