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Purpose Smartphones have become a critical medium by which people remain in contact with family, friends, and colleagues. A variety of factors have contributed to the rapid prevalence of smartphones. The most influential factor is definitely the mobile platform or mobile operating system (OS). The purpose of this paper is to employ a theoretical framework based on an information systems success model and a theory values to examine the factors that affect smartphone users’ switching mobile OSs. Habit is regarded as a moderating variable to construct an integrated research model which helps researchers unveil the puzzle of users’ switching mobile OSs. Design/methodology/approach The proposed model was empirically evaluated using survey data collected from 424 users on their perceptions of smartphones. A structural equation modeling was used to assess the relationships of the research model. Findings The results of the study have shown that users consider the perceived switching value and satisfaction arising from their behaviors when deciding whether to switch to another mobile OS. Quality characteristics, including information quality, system quality, and service quality, are the key factors determining people’s perceived switching value and satisfaction. The participants of the study were grouped by degree of habit. The effect on satisfaction was not significant in the high-habit subgroup. Nevertheless, it has been found that the influence of the perceived switching value was stronger in the low-habit subgroup than in the high-habit subgroup. Originality/value This study contributes to a theoretical understanding of factors that explain users’ intention to switch mobile OSs.

The previous existing mobile technologies were only limited to voice and short messages, organized between several network operators and service providers. However, recent advancements in technologies, introduction, and development of the smartphones added many features such: high-speed processors, huge memory, multitasking, screens with large-resolution, utile communication hardware, and so on. Mobile devices were evolving into general-purpose computers, which resulted in the development of various technological platforms, operating systems, and platforms for the development of the applications. All these results in the occurrence of various competitive offers on the market. The above-mentioned features, processing speed and applications available on mobile devices are affected by underlying operating systems. In this paper, there will be discussed the mobile operating systems and application development platforms. 

Based on the research of XML core technology, this paper introduces the current mainstream XML parsing methods, XML DOM and SAX methods. For the current android system, which is used in android operating system. XML is researched in the android layout file, which is combined with the current android platform. The paper introduce application of XML pull parsing process, first study the parsing performance of XML, the second through an example to discuss these three methods, the last analysis XML file Size and compares these three methods.

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.

Although many efforts have been made through past years, skin cancer recognition from medical images is still an active area of research aiming at more accurate results. Many efforts have been made in recent years based on deep learning neural networks. Only a few, however, are based on a single deep learning model and targeted to create a mobile application. Contributing to both efforts, first we present a summary of the required medical knowledge on skin cancer, followed by an extensive summary of the most recent related works. Afterwards, we present 11 CNN (convolutional neural network) candidate single architectures. We train and test those 11 CNN architectures, using the HAM10000 dataset, concerning seven skin lesion classes. To face the imbalance problem and the high similarity between images of some skin lesions, we apply data augmentation (during training), transfer learning and fine-tuning. From the 11 CNN architecture configurations, DenseNet169 produced the best results. It achieved an accuracy of 92.25%, a recall (sensitivity) of 93.59% and an F1-score of 93.27%, which outperforms existing state-of-the-art efforts. We used a light version of DenseNet169 in constructing a mobile android application, which was mapped as a two-class model (benign or malignant). A picture is taken via the mobile device camera, and after manual cropping, it is classified into benign or malignant type. The application can also inform the user about the allowed sun exposition time based on the current UV radiation degree, the phototype of the user’s skin and the degree of the used sunscreen. In conclusion, we achieved state-of-the-art results in skin cancer recognition based on a single, relatively light deep learning model, which we also used in a mobile application.

Many two-sided platforms, such as eBay, iOS, Android, and Twitter, invest in developer integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, in order to reduce the cost and improve the functionality of third-party content developed for the platform. Although these integration tools are crucial to platform success, they are costly to create, and therefore, managers need to understand where and when to deploy them. In particular, when the necessary integration investment is high, the advice to subsidize one side of a two-sided market while charging the other may not hold. This means that integration investment should be carefully coordinated with market pricing decisions. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in high-consumer value markets. However, there are nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that such investments are complements, not—as one might expect—substitutes. Many two-sided platforms (for example, eBay, Google, iOS, Android, Twitter, and Amazon) provide integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, to reduce the costs and improve the functionality of third-party content developed for the platform. The need for such investment is increasing with the rise of major new markets as the result of technologies, such as the “Internet of Things.” Although crucial to platform success, platform integration tools are costly to create. We develop an analytic model to explore the key tradeoffs behind investment in integration tools and how that investment interacts with pricing decisions in a two-sided market. We model these decisions for hardware/software platforms as well as hybrid retail platforms and analyze them under various scenarios, including monopoly and competition. Our results suggest that considering integration investment can create market regimes in which the standard pricing results from the extant platform literature no longer hold. For example, the tendency to reduce prices to one side of a market in response to increasing the benefit of the network to the other side may be suboptimal in the presence of integration investment. Therefore, integration investments must be well coordinated with pricing decisions made for both sides of the market. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in a market in which content providers earn a high-enough profit margin creating content that is highly valued by the consumer market. Hybrid retail platforms often show similar behavior. However, there are some nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that these investments are complements, not—as one might expect—substitutes. We conclude by discussing this work’s implications for theory and practice.

The launch of the new iPad Pro by Apple in March 2020 generated high interest and expectations for different reasons; nevertheless, one of the new features that developers and users were interested in testing was the LiDAR sensor integrated into this device (and, later on, in the iPhone 12 and 13 Pro series). The implications of using this technology are mainly related to augmented and mixed reality applications, but its deployment for surveying tasks also seems promising. In particular, the potentialities of this miniaturized and low-cost sensor embedded in a mobile device have been assessed for documentation from the cultural heritage perspective—a domain where this solution may be particularly innovative. Over the last two years, an increasing number of mobile apps using the Apple LiDAR sensor for 3D data acquisition have been released. However, their performance and the 3D positional accuracy and precision of the acquired 3D point clouds have not yet been fully validated. Among the solutions available, as of September 2021, three iOS apps (SiteScape, EveryPoint, and 3D Scanner App) were tested. They were compared in different surveying scenarios, considering the overall accuracy of the sensor, the best acquisition strategies, the operational limitations, and the 3D positional accuracy of the final products achieved.

With limited retrieval of reserves and restricted capability in plant pathology, automation of processes becomes essential. All over the world, farmers are struggling to prevent various harm from bacteria or pathogens such as viruses, fungi, worms, protozoa, and insects. Deep learning is currently widely used across a wide range of applications, including desktop, web, and mobile. In this study, the authors attempt to implement the function of AlexNet modification architecture-based CNN on the Android platform to predict tomato diseases based on leaf image. A dataset with of 18,345 training data and 4,585 testing data was used to create the predictive model. The information is separated into ten labels for tomato leaf diseases, each with 64 × 64 RGB pixels. The best model using the Adam optimizer with a realizing rate of 0.0005, the number of epochs 75, batch size 128, and an uncompromising cross-entropy loss function, has a high model accuracy with an average of 98%, a strictness rate of 0.98, a recall value of 0.99, and an F1-count of 0.98 with a loss of 0.1331, so that the classification results are good and very precise.

With the availability of Global Navigation Satellite Systems raw measurements in smartphones, high-precision positioning using smartphones has become possible in recent years. Integer ambiguity resolution (IAR) is critical for smartphone precise positioning, which would be more difficult in smartphones and affected by various factors. In this paper, we will numerically study the effect factors for integer property of phase ambiguities, data quality, IAR efficiency and positioning accuracy for the smartphone. The results show that integer property of phase ambiguities and data quality are governed not only by the smartphone brands and embedded antennas, but also by the mobile operating system and smartphone attitudes. In general, the different constant offsets exist for the different frequency ambiguities, and the ambiguities are fixable once the corresponding offsets are calibrated. With the operating system of EMUI 9.0, the ambiguities are fixable for Xiaomi Mi8 but not for Huawei Mate20. However, with the updated operating system of EMUI 9.0.1, the ambiguities of Huawei Mate20 become fixable. Besides the smartphone brands and embedded antennas, the smartphone attitudes significantly affect the data quality, such as carrier-to-noise density ratio ( C / N 0) values, data availability and observation precisions, thus affecting the ambiguity fixing rate and positioning accuracy. The ambiguity fixing rates differ from attitudes by 17%, and generally, the upward attitude has the best performance. Finally, the kinematic positioning results indicate that only the meter-level accuracy is obtained with an embedded antenna, while the centimeter to decimeter-level accuracy is achievable with the external antenna.

Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency-Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package's (Apk's) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can't accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample's detection accuracy is 92.71%. Compared against other state-of-the-art approaches, the proposed approach is more effective by detecting malware and malware families.