A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

Bluetooth/GSM Based Android Controlled Robot

The project aims in designing a robot that can be operated using Android Apps. The controlling of the Robot is done wirelessly through Android smart phone using the Bluetooth module feature present in it. Here in the project the Android smart phone is used as a remote control for operating the Robot. Android is a software stack for mobile devices that includes an operating system, middleware and key applications. Android boasts a healthy array of connectivity options, including Wi-Fi, Bluetooth, and wireless data over a cellular connection (for example, GPRS, EDGE (Enhanced Data rates for GSM Evolution), and 3G). Android provides access to a wide range of useful libraries and tools that can be used to build rich applications. Bluetooth is an open standard specification for a radio frequency (RF)-based, short-range connectivity technology that promises to change the face of computing and wireless communication. It is designed to be an inexpensive, wireless networking system for all classes of portable devices, such as laptops, PDAs (personal digital assistants), and mobile phones. The controlling device of the whole system is a Microcontroller. Bluetooth module, DC motors are interfaced to the Microcontroller. The data received by the Bluetooth module from Android smart phone is fed as input to the controller. The controller acts accordingly on the DC motors of the Robot. The robot in the project can be made to move in all the four directions using the Android phone. The direction of the robot is indicated using LED indicators of the Robot system. In achieving the task, the controller is loaded with a program written using Embedded ‘C’ language.

Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

Applications of Mobile Learning and Transactional Distance Theory in the Context of Higher Education during COVID-19 Pandemic

ABSTRACT Mobile learning, which can be performed through numerous applications that run on smartphones, personal digital assistants, and other devices, has played a major role in education, especially during under the restrictive conditions due to the COVID-19 pandemic. This paper is a review of literature of mobile learning efficiency in education. Some application of m-learning in education have shown extensive success. Mobile learning could meet with success in a suitable learning environment. The recently developed transactional distance theory focuses on three elements in the connection and involvement in the distance education environment, namely, the teacher, the student, and dialogue. The core of that theory is investigating the ways that the learner can progress to becoming a self-directed learner with the support of the teacher. Though there are advantages to m-learning, there are some disadvantages of m-learning such as technical, physical and health issues

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

A Medical Mobile Application For Managing A Heart And Lung Machine During Cardiac Surgery

Background: Opened heart surgery with cardiopulmonary bypass (CPB) is a critical and complex procedure. A Heart-Lung machine (HLM) plays an important role for controlling the cardiopulmonary functions during the time of the surgery. Perfusionist must consider a variety of essential factors and calculate several cardiovascular parameters regarding the process of operating a HLM. To improving the quality of work, personal digital assistants must continually develop their skills and knowledge levels.Objective: The goal of this work is to construct a mobile application device that has a wide variety of functions which has the capacity to control targeted clinical planning and decision making for HLM users so to enable them to have control and evaluate the mobile application to user’s satisfaction. Methods: This smartphone app was constructed base on the ionic framework. The researchers have developed an unique algorithms for operating the HLM. The app was generated according to the phase of design, algorithm, validation, and user’s satisfaction of perfusionists.Results: The Project Researchers have officially assigned this medical mobile application with the name is Perfusion Assistant app that can be accessed and used effectively cross platform on iOS and Android. The application is comprised of five main categories which includes: a perfusion calculator, myocardial protection chart, drugs details, priming solution and parameters values. Result shown that all cardiovascular parameters did not significant differ from Perfusion Assistant app when compared to manual calculation. User’s satisfaction was at 3.64±0.76 in the first evaluation. After modification with feedback from experts, the app was evaluated with a 4.13±0.56 satisfaction. Conclusions: Perfusion Assistant app is an application designed in clinical planning and decision of HLM controlling for perfusionists and medical staff that work in an opened heart surgery arena. Perfusion Assistant app offers a variety of calculations related to CPB including blood flow rate, systemic vascular resistant, priming volume, and predicted hematocrit. Furthermore, Perfusion Assistant app provides a quick, easy access, and real-time application for CPB that user’s satisfaction was a good level.

Safety in the administration of injectable medications: Scoping review

Objective: to raise scientific evidence on technologies for the safe administration of injectable drugs in hospitalized adult patients. Method: this is a study of the Scoping Review type, using the crossing of the descriptors Patient Safety, Medication Errors and Technology in the following databases: Medical Literature Analysis and Retrieval System Online Electronic Library Online, Science Direct, Virtual Health Library, Pubmed, Web of Science and Scopus. The choice of studies included in the sample and data extraction occurred in a paired manner by independent researchers. Results: 14 studies were classified as eligible. The interventions found were medication administration using a barcode, patient identification by plates, bracelets, radiofrequency and/or biometrics, personal digital assistants, mobile nursing carts, electronic medication administration record, safety syringe system based on key-lock adapters, smart infusion pumps, drugs organizer kits, storage bag, trays with dividers, adhesive in different colors for separating medicines by class, and double checking. Final considerations: The implementation of technologies, professionalism, changes in the organization and in the workplace linked to the empowerment of the patient and family/companion enables the quality of the injectable medication administration processes. It is considered an advantageous investment in technologies for the culture of safety.

Detection of COVID-19 from X-rays using Hybrid Deep Learning Models

In this paper, a hybrid deep learning model is proposed for the detection of coronavirus from chest X-ray images. The hybrid deep learning model is a combination of ResNet50 and MobileNet. Both ResNet50 and MobileNet are light Deep Neural Networks (DNNs) and can be used with low hardware resource-based Personal Digital Assistants (PDA) for quick detection of COVID-19 infection. The performance of the proposed hybrid model is evaluated on two publicly available COVID-19 chest X-ray datasets. Both datasets include normal, pneumonia and coronavirus infected chest X-rays. Results show that the proposed hybrid model more suitable for COVID-19 detection and achieve the highest recognition accuracy on both the datasets.

Students’ Acceptance of Teaching Interaction through the Diversity of Media in M-Learning

The technological and telecommunication development of this decade has had a profound impact, especially on the education system. In relation to this development, mobile technology in learning or M-learning is a new concept in the learning process. Examples of portable technology include PDAs (Personal Digital Assistants), Smartphones, iPads, Tablets, and more. Thus, the availability of these advanced technologies promotes an environment of m-learning among students as it is easy to carry, lightweight, and not burdensome. Through the applications of Google, Facebook, YouTube, Edmodo, Twitter, WhatsApp, and Instagram, information is available anytime and anywhere and to anyone. The objectives of this study were to identify the level of students’ knowledge of m-learning, identify students’ perceptions of m-learning, identify the applicability of students' application to m-learning and identify the relationship between students' level of knowledge and applications usage of m-learning. This questionnaire was used in the data generation, which was analyzed descriptively by using statistical Package for the Social Version 20 (SPSS Statistic 20). The respondents of this study were 204 students in the first year of Faculty Technical and Vocational Education. The finding of the study showed that the use of m-learning in the teaching and learning process has a positive impact which had a min value of 4.00 and above. Besides, this study showed that the use of m-learning is highly recommended as it provides a more engaging learning experience for students. Researchers have suggested that its use of m-learning includes urban and rural students in line with the government's goal of developing an innovative and competitive convergence-based generation.

PERSONAL DIGITAL ASSISTANTS IN MEDICAL EDUCATION AND HEALTH CARE

The purpose of this article is to study the functional capabilities and field of application of digital personal assistants in medical education and healthcare. The author of the article confirms that medical students and doctors use digital assistants to treat patients, obtain medical information and data on diseases, their symptoms, the dosage of appropriate drugs, as well as personal use. It is important to note that digital personal assistants are effectively used in a healthcare organization, but they must be integrated into existing systems in the organization and connected to the network for communication and data sharing. The article uses methods of complex theoretical and descriptive analysis. The scientific novelty of the work is that students and doctors have been found to use digital personal assistants to obtain drug and clinical information, to support clinical decision-making, to prescribe treatment to patients, to view laboratory results on wireless communication. Doctors use digital assistants to collect, modify, store patient data in the process of providing medical care, after which all information is synchronized with the central computer. The author notes that the nature of the use of digital assistants in medicine depends on factors such as functionality, an electronic platform, data security, their confidentiality, and functions in the medical field. Of course, novice physicians and students use personal digital assistants, especially in the process of continuous medical training. The practical significance of the work is due to the fact that the author conducted a study that found that digital assistants provide alternative ways of training, help future doctors systematize medical information, support medical decisions, including in the process of prescribing diagnostic studies and treatment, and lead to a reduction in medical errors.