Smart Homes using Android

Smart Homes using Android

The main objective of this project is to develop a simple and cost efficient home automation system with an Android application which would act as a remote and make at most use of all the appliances that can be connected which would help to simplify daily life. The idea of conserving electricity means to avoid wasting it and its minimum utilization. This means doing simple things like turning off the electrical appliance when any person is about to leave the room etc. The biggest motivation for the people to conserve electricity is for the accumulated savings in their energy bills at the end of the year. Now a days due to the Unplugging of fans and lights when it is not in use, most of the energies are wasted. This is happened mainly because most of the switches are located far away from the users. Modern houses are gradually shifting to centralized control system, involving wireless controlled switches. Basically, every single appliance in your house can be controlled using a remote device. Android based E-home is an application of embedded system which integrates Android operating system, Arduino controller and the Bluetooth for the implementation of Smart Home. Any Android device can act as a transmitting device. The user can control any appliance through a user friendly mobile application built in Android platform. In order to avoid this situation, we have proposed a system using Bluetooth and Android application which help users to control the fans and lights within a certain distance from them. Also nowadays all the new homes are shifting towards LED lights so by using our designed app and system we can control the intensity of these lights and also apply it in other applications like fan, motor etc Code Shoppy
Due to tremendous growth in the present day technologies, humans are adapted to these technologies in numerous ways. The process of transferring information from one point to another is known as Communication. Wireless communication or wired serial communication are two its types. Among these wireless communications have proved to be more popular and has received huge appreciation from all parts of the world. To provide security to the users, is the main reason behind this. Bluetooth technology stands on the top among the different wireless technologies as it is able to provide a communication between devices and users in a simple and efficient manner. In our everyday life, there are many types of Bluetooth devicesthat are being used. To control various appliances, several types of Bluetooth modules are designed. The modules are based on several specifications based on which they perform the operations that are related to it. These Bluetooth modules, operate at 2.4 GHz frequency and work within a range of 45 meters. With the help of this Bluetooth technology, we are designing a home automation system. The HC-05 Bluetooth module is used here. Some issues are needed to be considered before designing, the user should be able to connect to that Bluetooth module from any device he would wish to. He should also be able to change the host from one device to another and that module should work accordingly. If any fault occurs it should be able to detect it and the system should work immediately towards its solution when an instruction is given to improve the nature of wireless technology.
 

The system will facilitate users to pair any sort of Android devices with Bluetooth Sensor integrated on board with the respective appliances present in the room of the house, office or any place where it can be applied. The designed system is useful for the physically disabled people, people having a bed rest and also old people for whom getting up from the chair or bed just to switch on the appliance is not possible, so can be useful for the purpose since it is reliable and fast
https://codeshoppy.com/php-projects-titles-topics.html 

Real-Time Malware Detectors on Android

Real-Time Malware Detectors on Android

Android has become the leading operating systemfor next-generation smart devices. Consequently, the number ofAndroid malware has also skyrocketed. Many dynamic analysistechniques have been proposed to detect Android malware.However, very few of these techniques use real-time monitoringon user devices as Android does not provide low-level informa-tion to third-party apps. Moreover, some techniques detect aspecific malware class more effectively than others. Therefore,end users can be benefited by installing multiple malwaredetection techniques. In this paper, we propose SpyDroid, areal-time malware detection framework that can accommodatemultiple detectors from third-parties (e.g., researchers and an-tivirus vendors) and allows efficient and controlled real-timemonitoring. SpyDroid consists of two operating system modules(monitoring and detection) and supports application layer sub-detectors. Sub-detectors are regular Android applications thatmonitor and analyze different runtime information using themonitoring module and they report the detection module abouttheir findings. The detection module decides when to mark an appas malware. Researchers and antivirus vendors can now publishtheir techniques via app markets and end users can install anynumber of sub-detectors as they require. We have implementedSpyDroid using the Android Open Source Project (AOSP) andour experiments with a dataset containing 4,965 apps show thatdecisions from multiple sub-detectors can increase the malwaredetection rate significantly on a real device.Code Shoppy


                                    


Among all smartphone operating systems, Android occupiesover 85% market share in 2017 [1]. Moreover, Android-powered devices such as cars, fridges, televisions, point ofsale (POS) terminals, and ATM booths are expected to flooduser markets within a few years. Due to the popularity ofthe Android ecosystem, malware writers are targeting Androiddevices exclusively and the number of malware for Androidsurged exponentially in 2017. Android implements a num-ber of security mechanisms to ensure the safety of deviceresources, e.g., the permission mechanism.The permission mechanism of Android is coarse-grainedand users are usually ignorant about the sought permissions.Researchers also proposed attacks that can bypass the per-mission mechanism [2], [3]. As a result, effective detectionof malware is very important to mitigate security threats inthe Android ecosystem. Unfortunately, antiviruses are not veryeffective due to the restrictive security model of Android thatdoes not let any app scan the runtime behavior of others.Researchers have made great efforts to improve the securityof Android and proposed a number of static and dynamicanalysis techniques. In static analysis, the Android applicationfile (apk) is decompiled to perform analysis, such as data flowanalysis, control flow analysis, API call analysis, byte N-gram,and fingerprinting. Studies [4] have shown that static analysisis becoming less effective day by day due to powerful trans-formation techniques (call graph obfuscation, dynamic codeloading, manifest cheating, metamorphism, polymorphism,etc.). They concluded that dynamic analysis is a necessarycomplement to static analysis as it is less vulnerable to codetransformations.Dynamic analysis is more effective as it can extract featuresthat represent unique patterns of execution. Interestingly, ac-cording to this study [5], over 98% of the new malware arein fact variants of an existing malware family. Google usesa dynamic analysis system called Google Bouncer that ana-lyzes apks submitted to them. Unfortunately, dynamic analysistechniques that execute Android apps inside an emulator alsosuffer from the fact that malware writers can detect emulatorsand thus evade detection. Hence, real-time monitoring onuser devices becomes necessary. In addition, end users arenot benefiting from these research as it is very difficult forthem to integrate the techniques into their devices. Moreover,sometimes a specific class of malware can only be detected bya single technique or a particular antivirus. Therefore, deviceowners can be benefited by employing multiple malwaredetectors on their devices.In this paper, we propose SpyDroid, a real-time malwaredetection framework that can deploy multiple malware detec-tors (we call them sub-detectors) on a real device. SpyDroidis designed as a part of the operating system and has twomodules for monitoring and detection. Sub-detectors monitorruntime information using the monitoring module and performanalysis to detect malware. They report their analysis resultsto the SpyDroid detector. The detector decides when to markan app as malware. A framework like SpyDroid can help third-parties (researchers and commercial vendors) to publish theirdetection techniques via application markets and users caninstall multiple sub-detectors to improve the security of theirdevices
https://codeshoppy.com/android-app-ideas-for-students-college-project.html

Activity Recognition Based on Deep Learning and Android Software

Activity Recognition Based on Deep Learning and Android Software

 Deep learning has been highly concerned by scientific research institutions and industry since it wasborn in 2006. Initially, the application of deep learning was mainly in the field of image and speech. Since 2011, researchers from Google research institute and Microsoft research have applied deep learning to speech recognition, resulting in a reduction of recognition error rate by 20%-30%. In 2012, IIya Sutskever and Alex Krizhevsky, students from Jeffrey hinton, used deep learning to beat the Google team in ImageNet, which reduced the error rate of image recognition by 14%. In June 2012, Google chief architect Jeff Dean and Stanford professor AndrewNgled the famous GoogleBrain project, which used 160,000 cpus to build a deep neural network, and applied it to image and speech recognition, and finally achieved great success. In addition, deep learning has gained a lot of attention in the search field. Nowadays, deep learning has been widely used in image, speech, natural language processing, CTR estimation, big data feature extraction, etc.Code Shoppy
The revolution of deep learning has been starting in most of fields. Such as image recognition, speech recognition,signal processing, face recommendation and so on. With the system of deep learning algorithm, customs officials can complete face recognition automatically. Compared with recognizing artificially before, this method can reduce errors obviously. The limitation of manual operation is that human will be tired and easy to make mistakes. What’s more, it probably changes the method of input gradually. With the enthusiasm of deep learning research, various open source deep learning frameworks are also emerging, including Tensor Flow (TF), Caffe, Keras etc. As the most popular frameworks, TF is a relatively advanced machine learning library, which allows us to design neural network structures easily, without writing complex code by ourselves. At the same time,the good portability of TFand the diversity of interfaces are the main reason

 Deep learning really benefits us to a large extent. And with the development of society, people depend on mobile electronic products more, especially mobile phones. The artificial intelligence of mobile phones makes it so powerful that to achieve more functions, such as mobile payment, recording the number of steps, face detection and fingerprint identification, temperature sensing, etc. Thesefunctionsare inseparable with sensors. At present, a variety of built-in sensors of smart phones, such as accelerometers, gyroscopes, magnetometers, direction sensors, etc., can sense different movements, directions and external environments, especially when monitoring the movement and position of the device. Original 3D data [1].This project used the built-in sensorsof the mobile phone as the data input terminal, and the TF-based deep learning framework to implement recognition of people's activities byusing the model obtained through massive training as the core and Android application and mobile phone hardware.https://codeshoppy.com/php-projects-titles-topics.html

In conclusion, we can acquire the datathrough the mobile phone, then use the TF model that is transplanted to classify. At last,the probability of each activity is displayed on the mobile phone. In the future, we will continuously collect data to optimize the classification model in order to improve the accuracy of activity recognition. In addition, this project can be used in monitoring abnormal activities of the aged such as falling, or for calculating daily exercises. But in this project, the phone must have gyroscope or gravity sensor. It is necessary to consider applying this project into other phones by some sensors such as sports bracelets.The study of this experiment can be used as a stage step, and it can be used as a part of data monitoring in future research.