Android Smartphone Application Research Articles

DPXception: a lightweight CNN for image-based date palm species classification.

Date palm species classification is important for various agricultural and economic purposes, but it is challenging to perform based on images of date palms alone. Existing methods rely on fruit characteristics, which may not be always visible or present. In this study, we introduce a new dataset and a new model for image-based date palm species classification. Our dataset consists of 2358 images of four common and valuable date palm species (Barhi, Sukkari, Ikhlas, and Saqi), which we collected ourselves. We also applied data augmentation techniques to increase the size and diversity of our dataset. Our model, called DPXception (Date Palm Xception), is a lightweight and efficient CNN architecture that we trained and fine-tuned on our dataset. Unlike the original Xception model, our DPXception model utilizes only the first 100 layers of the Xception model for feature extraction (Adapted Xception), making it more lightweight and efficient. We also applied normalization prior to adapted Xception and reduced the model dimensionality by adding an extra global average pooling layer after feature extraction by adapted Xception. We compared the performance of our model with seven well-known models: Xception, ResNet50, ResNet50V2, InceptionV3, DenseNet201, EfficientNetB4, and EfficientNetV2-S. Our model achieved the highest accuracy (92.9%) and F1-score (93%) among the models, as well as the lowest inference time (0.0513 seconds). We also developed an Android smartphone application that uses our model to classify date palm species from images captured by the smartphone's camera in real time. To the best of our knowledge, this is the first work to provide a public dataset of date palm images and to demonstrate a robust and practical image-based date palm species classification method. This work will open new research directions for more advanced date palm analysis tasks such as gender classification and age estimation.

Empowerment Kader dan Perangkat Desa dalam Peningkatan Kapasitas Pertolongan Gawat Darurat Menggunakan Smartphone Android di Desa Pinggiran Sungai Kabupaten Banjar

The background of this activity is that the community in Pinggiran Sungai Village generally does not have adequate knowledge and skills in providing emergency assistance in emergency situations. The purpose of this activity is to provide training to cadres and village officials on the use of Android smartphone applications for emergency aid. The method used was training and simulation of the use of Android smartphone applications containing information and guidelines for providing cardiopulmonary resuscitation. The result of this activity is the provision of the community with knowledge and skills to provide emergency assistance for cardiac and pulmonary arrest using smartphone applications. It is expected that the community of Pinggiran Sungai Village will have better preparedness in dealing with health emergency situations.

Telehealth DM Group Android Smartphones in Controlling Blood Sugar Levels in Patients with Type 2 DM

Diabetes mellitus (DM) is a degenerative disease with an increasing number that continues to grow from year to year. Data from the International Diabetes Federation (IDF) shows that the prevalence of diabetes patients will increase to 84%, from 82 million to 151 million. Uncontrolled blood sugar levels can cause serious diseases that affect the heart, blood vessels, eyes, kidneys, and nerves. Aim: Knowing the effectiveness of the Telehealth DM Group application in controlling blood sugar levels in DM type 2 patients. Method: This study uses Research and Development (R&D) and quasi-experimental development methods with a pre-post control group design and uses a random sampling technique. Results: The Telehealth DM Group Smartphone Android application is feasible based on expert validation tests with marks of 93.33%. There is a difference in blood sugar levels before being given pre-intervention (219 mg/dl). After being given post-intervention (160 mg /dl) with a value of 0.002, the Telehealth DM Group application can control blood sugar levels of 58.6 mg/dl with a value of 0.004. Conclusion: Telehealth DM Group Android smartphones are effective as an educational and health service media in controlling blood sugar levels in patients with type 2 diabetes mellitus.

Remote Control Car System using Bluetooth HC-05 with Android Smartphone

Remote control car is a miniature car with a smaller size, which is controlled directly by remote or joystick using the HC-05 bluetooth module with the android smartphone application. Making a remote control car using Wemos R1 D1 as a microcontroller and four DC motors to drive the wheels of a remote control car. At this writing it was done to test the remote control car system with an application created using the MIT APP Inventor. In this test it was found that the remote control car system can work well at a distance of 0-20 meters and there is a difference in delay for each command used.

Media Literacy Based Android Application to Introduce Sumbawa Culture for Visual Disabilities

This research aims to introduce Sumbawa culture to blind disability based android smartphone applications that can utilize feature reader screens and barcodes on objects historical. Output from application This is information in the form of audio details of cultural sites in the Inner Palace Loka, Bala Kuning Palace, Bala Puti Palace, Bala Datu Ranga, and the Sumbawa Museum. This research is quantitative method with build uses waterfall method and design with UML models with test six aspect appropriateness to application. The resulting average value for the attractiveness p ax is 1.90 and the average score for as a perspicuity peak of 1.77. Third aspect from UEQ, efficiency value with an average of 1.61 and dependability aspects with value 1.55. Whereas the stimulation aspect on the UEQ model is 1.49 and the novelty aspect with the average value is 1.39. From testing aspect perspicuity, efficiency, dependability, stimulation and novelty show quality application are excellent. Whereas the attractiveness aspect generates very good value. Application literacy culture for blind disability is something effort in preserving culture and can be accessible on google play store.

Design and Build an Android Based Gateway Controller using the MQTT Protocol

The gate is the most important component of a building's security system. Although almost all homes have gates, they are still opened and closed in the traditional manner. As technology advances, a remote control may be used to automatically open and close the gate. In this study, the author attempts to develop a gate utilizing an internet-connected microcontroller and an android smartphone application to make it simpler for users to manage and monitor the motor's status and eliminate the need for a remote control. The automatic gate prototype includes a track length of 3 meters, a 12 V dc gearbox motor, and a control system. The gate load standard is 15.40 kg. The study's findings employ the Internet of Things (IoT) to regulate and observe gate opening and closing as well as gather data on gate status and maintenance. According to test results, an average torque value of 78.44 Nm at an average current value of 10.25 A was obtained utilizing a mass load variation between 1 and 15 kg. Qos performance between Node MCU and Android application shows the quality of data transfer during deceleration with an average value of 34.3 seconds. The average throughput value is 149.6719 bps.

Testing the validity and reliability of a new android application-based accelerometer balance assessment tool for community-dwelling older adults

BackgroundPostural instability is a risk factor for falls in older adults. It is possible to detect postural stability using an integrated accelerometer (ACC) sensor in a smartphone. Therefore, a novel ACC-based smartphone application running on the Android operating system called BalanceLab was created and tested. PurposeThis study aimed to determine the validity and reliability of a novel ACC-based Android smartphone application for assessing balance in older adults. MethodsUsing BalanceLab, 20 older adults completed three balance assessments: the Modified Clinical Test of Sensory Interaction in Balance (MCTSIB), a single-leg stance test (SLST), and a limit of stability test (LOS). The validity of this mobile application was investigated using a three-dimensional (3D) motion analysis system and the Fullerton Advanced Balance (FAB) scale. The test-retest reliability of this mobile application was determined on two separate occasions within one day, at least two hours apart. ResultsThe two static balance assessments (the MCTSIB and SLST) demonstrated moderate to excellent correlation with the 3D motion analysis system (r = 0.70–0.91) and the FAB scale (r = 0.67–0.80). However, the majority of the dynamic balance tests (the LOS tests) did not demonstrate any correlation with the 3D motion analysis system or the FAB scale. This novel ACC-based application demonstrated good to excellent test-retest reliability (ICC = 0.76–0.91). ConclusionA static, but not dynamic, balance assessment tool that uses a novel ACC-based application for Android can be used to measure balance in older adults. This application has moderate to excellent validity and test-retest reliability.

IoT-based child tracking using RFID and GPS

With growing concerns about their children’s safety and security, parents have shown an increasing interest in creating a dependable system that allows them to track and monitor their children in outdoor environments. The number of children who have gone missing, particularly in public areas, has risen, making it even more necessary to develop efficient solutions. This study focused on designing and implementing an affordable Internet of Things-based system that enables parents to track their children’s movement while they are in outdoor environments. The system described in this study relies on the use of radio frequency identification (RFID) readers installed in multiple locations to detect the presence of a child within a designated area of interest. To connect parents with this system, an Android smartphone application was developed and connected to the Thinger.io platform. The application displays the child’s location on a map using global positioning system technology and sends an alert message through the global system for mobile networks via 3G as soon as the RFID reader detects the child’s mobile tag. The system was tested within the designated area of interest to evaluate its performance. The results revealed that the RFID reader was able to detect the child’s movement within a range of approximately 4.5 meters from each RFID device and 9 meters between two RFID devices. Furthermore, the system was able to accurately determine the child’s real-time geolocation, both with and without Internet access. The system was also found to be lightweight and cost effective.

Design Development of Detection System and Ro-Ro Ship Notification based on Fuzzy Inference System

Ship stability is very important for the safety of ship motion. There are many factors that affect the stability of a ship. One of the causes of accidents on ships is the problem of ship stability, including the ship cannot be controlled, and loses balance due to improper placement of cargo loads. This study combines gyroscopes, accelerometers, compasses, and GPS sensors, so that more accurate ship tilt information is obtained through an Android smartphone application. This study uses the fuzzy inference system (FIS) method with a trapezoidal membership function where there are 2 inputs and 1 output. Ship tilt input uses 3 linguistic variables very tilted, tilted, and stable. The slope duration input uses 5 very fast, fast, fairly fast, slow, and very slow linguistic variables. Ship status output is divided into 3 linguistic variables safe, alert, and dangerous. Testing and implementation with an input slope of 4.8 and a slope duration of 10 seconds using the Sugeno fuzzy method, the ship's crips value of 0.65 with an alert status was obtained. Calculation of the accuracy of the gyroscope sensor error using the MAPE method, the result is an error percentage of 6.55% (very good). The system accuracy error of 39 trials (36 correct and 3 incorrect) is 92.30% (very good). This research is expected to make it easier for the captain to monitor the stability of the ship and can provide notification of the status of the ship to the captain of the ship if there is a condition of the ship that needs to be watched out for. In addition, the notification will also be received by Port officers on land.

Security System For Motorcycle Using Raspberry Pi and Fingerprint With Mobile-Based Finite State Machine (FSM) Method

Data from the BPS (Central Statistical Agency) show that private car theft in Indonesia is on the rise year after year, especially for two-wheeled vehicles or motorcycles. According to the BPS Crime Statistics Report, there were around 35,000 motor vehicle theft events in Indonesia in 2017 and 38,000 instances in 2018. People's concerns regarding the security of these two-wheeled vehicles are growing as a result. This study presents a fingerprint- and Raspberry Pi-based motorcycle security system. Bikes may be monitored, and their ignitions can be controlled remotely using a mobile-based Finite State Machine (FSM) technique. The Neo 6 GPS module will send longitude and latitude in real time to the Android smartphone application, which generates a map or location point so that it will send notifications if there is movement on the motorcycle. The voltage sensor connected to the Raspberry Pi circuit can detect the motor voltage. A system that uses the FSM (Finite State Machine) approach to identify changes in location data from designated parking lots and deliver messages to a push bullet is the study's output. Controlling the motorcycle ignition, which may remotely switch off the motorcycle when one is taken, is another way to provide early notice.

Development of electrocardiogram intelligent and wearable monitoring system-assisting in care

<p>The primary factor contributing to the high mortality rate in our country is coronary heart disease, which affects almost 50% of people from rural regions. Internet of things (IoT) contributes effectively to the development of point of care (POC) gadgets that support the medical upkeep of an expanding agricultural population. An electrocardiogram test is crucial for analysing cardiac disorders. Therefore, we must develop a POC piece of hardware to assess the health of the heart in an affordable manner and to design it for the patients without interfering with their daily regular procedure in order to monitor the patient's coronary heart disease. As a result, we must design an uninterrupted workbench, which consists of three main integrated parts. The first is a mobile Bluetooth low energy device that has 5-lead electrocardiogram (ECG) surveillance equipment and the smallest form factor. The smart phone Android application that inherits, resolves, and maps the data sent from the ECG device comes next. The patient's information and report details are then compiled on a cloud server for the doctor's future attribution needs.</p>

Identification of North American Nursing Diagnosis Association-Nursing Interventions Classification-Nursing Outcomes Classification of nursing home residents using on-time data by android smartphone application by registered nurses.

We aimed to investigate the nursing process linkages formed by Nursing Interventions Classification (NIC) and Nursing Outcomes Classification (NOC) according to the primary NANDA-I diagnoses by registered nurses (RNs), customized to nursing home (NH) residents in Korea, using a developed smartphone application for NH RNs. This is a retrospective descriptive study. Applying quota sampling, a total of 51 NHs from all operating 686 NHs hiring RNs participated in this study. Data were collected from June 21 to July 30, 2022. Data on NANDA-I, NIC, NOC (NNN) of nurses applied to the NH residents were collected through a developed smartphone application. The application consists of general organization and residents' characteristics, NANDA-I, NIC, and NOC. RNs selected randomly up to 10 residents and NANDA-I with risk factors and related factors over the past 7 days, followed by all applied interventions out of 82 NIC. RNs then evaluated residents through 79 selected NOC. We found the frequently used NANDA-I diagnoses, Nursing Interventions Classifications and Nursing Outcomes and Classifications applied for NH residents by RNs and developed the top five NOC linkages used to build care plan. It is time to pursue high-level evidence and reply to the questions raised in NH practice using NNN with high technology. The outcomes for patients and nursing staff are improved by the continuity of care made possible by uniform language. NNN linkages should be used to construct and utilize the coding system of electronic health records or electronic medical records in Korean long-term care facilities.

Strategi Optimalisasi Penggunaan Layanan Mobile Banking Pada PT. Bank Sumut Syariah KCP. Stabat

This study aims to determine the strategy of the north sumatra bank in optimizing mobile banking services at PT. Bank Sumut Syariah Stabat. This type of research is descriptive research using qualitative methods. The research data is sourced from primary data and secondary data. Data collection techniques in this study used observation, interview and documentation methods. The results of this study show that the strategy of sharia banks in optimizing mobile banking services through several ways and stages, one of which is by issuing an application called "SUMUT Mobile". The launch of this application aims to make customers of sharia sumut banks not bored and always comfortable in making transactions on mobile banking services This application can be accessed through the Android Smartphone application. In this application, there are many features such as fund transfers, balance checks, electricity token payments, credit purchases and so on which will certainly make it easier for users to make transactions.

A Prototype of Garbage Picker Ship Robot Using Arduino Nano Microcontroller

This study aims to create a robot prototype for a garbage collection ship made using an Arduino nano microcontroller. The prototype of this garbage collection robot vessel is controlled by an Android smartphone application that is connected via Bluetooth. The prototype of the robot of the boat was made to deal with piles of garbage that can cause flooding. The method used in making the prototype ship robot along with the controller application begins with identifying the problem, namely the problem of piles of garbage, then needs analysis. Moreover, in this research, what will be developed is in terms of design, ship robot prototype, and ship robot controller design. Next is implementation and testing. The prototype of the garbage collection ship robot uses an Arduino nano microcontroller and in making a controller application using Android Basic 4. Then the trial stage was carried out on an Android smartphone. The results of this study are a prototype ship robot that can move according to commands through a controller application.

ОТСЛЕЖИВАНИЕ НЕРОВНОСТЕЙ ДОРОГ ПРИ ПОМОЩИ ANDROID-СМАРТФОНА

In order to improve the quality of the road surface, as well as improve the quality of logistics, speed up cargo transportation, increase the level of safety of transported goods and reduce the number of road accidents, the author conducted a study of available means for monitoring road irregularities. A survey among motorists showed that car users do not have access to sources about road surface unevenness or do not know about available sources of information about the quality and features of the road surface, and would also like to have a publicly available source of such information. The presence of unevenness on the road surface along which the route of a car or truck passes increases the time it takes to complete the route and the risk of damage to goods transported by transport. To reduce the number of road accidents and damage to goods delivered by ground transport, an application for Android smartphones was developed that allows you to obtain information about bumps on the roads through markers installed on the map, and the application also allows the user to track bumps to increase the coverage of tracked bumps.

Stereolithography 3-D Printed Electrochemiluminescence Platform With Random Grade Graphite Electrodes: Detection of HO and Cholesterol Using a Smartphone

The growing demand for low-cost mini biosensing platforms combining important features, such as fast performance, rapid prototyping, portability, and easy integration in point-of-care settings, has proven their existence in the field of targeted biomolecule detection. This work encompasses a novel, simple, and ultrafast fabrication of a stereolithographic 3-D printed electrochemiluminescence (GP-SE-ECL) biosensor with a graphite pencil-based single electrode (SE) to detect H2O2 and cholesterol. Graphite pencil electrodes were extensively used as a replacement for screen-printing, laser-induced graphene-based electrodes due to their user-friendliness, ease of availability, excellent electrochemical properties, and cost-efficiency. Different graded graphite electrodes (F, H, HB, 3B, 6B, and 8B) were used to fabricate SE electrochemiluminescence (ECL) devices. Since the conductivity values and surface area for different graded pencils vary, the impact of conductivity and surface area of the electrodes on the effectiveness of the ECL device was confirmed with optimized parameters by sensing H2O2. The lab-made, portable 3-D printed dark room platform integrated with a smartphone was used to capture the ECL signals. The smartphone android application was developed which can not only capture the ECL images, but also calculates the intensities values for the region of interest. Finally, the performance of the fabricated GP-SE-ECL biosensor was corroborated for cholesterol sensing to obtain a linear range (LR) from 50 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1000 ~\mu \text{M}$ </tex-math></inline-formula> and a detection limit of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.71 ~\mu \text{M}$ </tex-math></inline-formula> . Based on the experimental data, it was confirmed that the fabricated device with random grade pencil possesses strong acceptability in the field of biomolecule detection.

An Android Application-based Surface Oust Classifier

Surface dust can be a source carrier of viruses, bacteria and air pollution which entail common health issues such as asthma attacks, chest tightness, wheezing and difficulty in breathing. Visually perceived, cleanliness is one measure of indoor air quality and is the subjective assessment of cleaning quality. The aim of this work is to use pattern recognition mediated through a mobile application to analyse and classify dust in households, in order to obtain useful information about the dust sources for the selection of appropriate countermeasures in view to improve air quality and better manage the cleaning of indoor surfaces. The dust type categorization in this work are pollen, rock and ash. This paper also explores the concept of transfer learning techniques and adopts it for small particle classification using CNN models by developing a surface dust application for android smartphones. The behaviour of InceptionV2, InceptionV3, ResNetV2, MobileNetV2 and MobileNetV3 as dust feature extractors were analysed based on their accuracy, precision, recall and Fl-Scoreperformance metrics. Results show that MobileNetV3 model is best suited as a dust feature extractor and rapid dust prediction with an accuracy of up to 92% and low-size storage of only 30 megabytes. Additional Keywords: Android; Classification; Transfer Learning; Smartphone; Surface dust.

An Android Application-based Surface Dust Classifier

Surface dust can be a source carrier of viruses, bacteria and air pollution which entail common health issues such as asthma attacks, chest tightness, wheezing and difficulty in breathing. Visually perceived, cleanliness is one measure of indoor air quality and is the subjective assessment of cleaning quality. The aim of this work is to use pattern recognition mediated through a mobile application to analyse and classify dust in households, in order to obtain useful information about the dust sources for the selection of appropriate countermeasures in view to improve air quality and better manage the cleaning of indoor surfaces. The dust type categorization in this work are pollen, rock and ash. This paper also explores the concept of transfer learning techniques and adopts it for small particle classification using CNN models by developing a surface dust application for android smartphones. The behaviour of InceptionV2, InceptionV3, ResNetV2, MobileNetV2 and MobileNetV3 as dust feature extractors were analysed based on their accuracy, precision, recall and F1-Score performance metrics. Results show that MobileNetV3 model is best suited as a dust feature extractor and rapid dust prediction with an accuracy of up to 92% and low-size storage of only 30 megabytes.

Stopkontak Pintar Berbasis Internet of things sebagai Solusi Manajemen Energi Listrik dengan Menggunakan Aplikasi Android

Leaving an electrical appliance connected to an outlet when not in use or on standby accounts for 80% of electricity wastage. A study conducted by the Lawrence National Laboratory, it was revealed that when a cellphone charger is still connected to a wall socket when not in use, there is still power being released. The purpose of this research is to make a smart socket based on the Internet of things (IoT) to be able to save electricity. An IoT smart socket that is designed to be integrated with an Android-based smartphone application. The IoT smart socket uses the ACS-712 current sensor and the ZMPT101B voltage sensor to measure electrical energy and then uses the Arduino Nano microcontroller and NodeMCU ESP8266 so that the socket can communicate with the Google Firebase cloud via Wi-Fi connection to the internet. The IoT smart socket uses a relay module as a switch, and the micro SD and RTC DS3231 modules record and store data (data logging) of electrical energy information that has been issued. The IoT smart outlet is integrated with Android smartphone applications created through Android Studio. The IoT smart socket in this study has 2 socket holes and features Wi-Fi pairing, electrical energy measurement, data logging, and socket scheduling (timer). The IoT smart outlet has an average measurement error of 3.27% when compared to a measuring instrument, and there is an error in the outlet timer feature with a delay of 5 to 30 seconds. The IoT smart socket is still said to be valid and can function properly.

Diabetes prediction using machine learning and explainable AI techniques.

Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.