Implementation and Customization of a Smart Mirror through a Facial Recognition Authentication and a Personalized News Recommendation Algorithm

Abstract: This project has been developed within the context of a time where every day we see more and more connected devices. The Internet transformed our lives by connecting us more easily to information and other people in the virtual world. Mobile phones then became smartphones and since then this concept has erupted and morphed into the Internet of Things, things that connect us to everyday objects. There is no end to objects that could be made “smarter”, some being more suited to this than others. Mirrors, for example, provide a large surface ideal for displaying information and interacting with it. Most people have mirrors at home so the concept of a smart mirror that you can interact with is attractive and has been fantasized about in many futuristic movies. Smart mirrors, such as Magic Mirror and Home Mirror, have been only developed by people in the Maker community, with varying degrees of interactivity. However, so far, the features of these mirrors have been limited. This final year project describes how a smart mirror was built from scratch using a Raspberry Pi for the hardware and custom software built on top of Raspbian, a Linux distribution. The goal of the project was to create a Smart Mirror device that people could interact with but also to further develop the technology so that it would let you install and develop your own applications for it. Overall, results were good because a higher level of interactivity has been achieved by being able to use voice commands, gestures, and smartphones. A few problems arose in the construction and software side of the project, such as the glass not being reflective enough and the gesture recognition being unreliable, but these drawbacks can be addressed by doing more tests and trials to further develop the Smart Mirror

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Smart appliance design that includes multimedia intelligence to deliver comfortable, convenient, and secure personal services in the home is becoming increasingly crucial in the age of information and communication technology. This research looks at the design and execution of a novel interactive multimedia mirror system called as "smart mirror." The glass that will be used is the foundation of the design of a smart mirror. Two-way glass is suggested because it allows the visuals on the display to be seen more clearly. Our way of life has evolved to the point where making the best use of one's time is critical. Based on user surveys and prototype implementation, we propose the development of an innovative appliance that incorporates interactive information services delivered via a user interface on the surface of a mirror. Our work is based on the assumption that we all check ourselves in the mirror before leaving the house, so why shouldn't the mirror be intelligent? Smart Mirrors will eventually replace regular mirrors, providing users with both mirror and computer-assisted information services as technology improves. Because of the Raspberry Pi microcontroller cards aboard, the devices can connect to the internet, download data from the internet, and show that data on the mirror. Weather data, time and location data, current event data, and user data gathered from web services using a Raspberry Pi 3 microcontroller card are all included in the designed intelligent mirror system. The mirror will light up when the user steps in front of it. When thinking about this project, phrases like Smart Mirror, Interactive services, Raspberry Pi , and Web services come to mind.

Preparing in front of the mirror in the morning takes time. This Smart Mirror can solve several business issues simultaneously. This voice service system analyzes customer inquiries and instructions using "ALEXA". Smart Mirror, based on the Raspberry Pi 4, is the newest design to replace our mirrors with high-tech and inventive applications. We have all seen various things that help the country and world thrive in this modern era. Multitasking is required since it is hard to fit everything into a day. The challenge is controlling everything that might impact a person to prepare for each day and accomplish all the critical chores in front of the mirror more efficiently.This project aims to construct a smart mirror that provides general information like news, time, weather, and other valuable data to the general public. This mirror collects this information throughout morning preparation to make it easier. Our items can incorporate music, control techniques, and other entertainment to make this mirror more fascinating. Smart glass can improve a modern lifestyle. Face recognition will enhance mirror use.

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

In today's world, Internet of Things (IoT) technology has revolutionized various fields – including agriculture – by offering smart solutions that enhance crop yield and quality. One crucial aspect of smart farming is the application of IoT for disease detection and prevention. Crop diseases caused by bacteria and viruses can have devastating effects, leading to significant financial losses for farmers. Early detection plays a crucial role in managing these diseases, and IoT technology provides reliable and efficient methods to achieve this goal. To aid in the research on disease recognition in corn or maize plants, a large-scale dataset comprising 4188 images across four disease categories – namely, healthy, common rust, gray-leaf spot, and blight – is utilized. This dataset is used to train various machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network (CNN), naïve Bayes (NB), gradient boosting trees (GBT), long short-term memory (LSTM), and the algorithms from recent literature such as genetic algorithm-based ensemble classifier, XGBoost, AdaBoost and XGBoost + KNN to accurately identify disease patterns in plants and is loaded into the Raspberry Pi. By interfacing a camera with a Raspberry Pi, leaf diseases can be detected, and the farm’s status can be sent to the farmer’s webpage using a Wi- Fi server. This information empowers farmers to take immediate action to control the disease such as activating sprinklers or applying pesticides. Through manually controllable on/off mechanisms, farmers can remotely manage these actions, ensuring the health and disease-free state of their crops. Among the selected algorithms, the XGBoost + KNN algorithm demonstrates the highest accuracy, reaching an impressive 98.77%.

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

Internet of things and Automation systems have taken over the world at present times. Home security has always been a field of concern among mass people. Making home security systems safer, cheaper and easy to understand is a challenge that many engineers around the world are striving to achieve in developing countries. Considering the economic condition of people of a developing country like Bangladesh, we propose an IoT based smart home security and automation system named `Facebook messenger Chatbot' which is made using Raspberry PI as the central processor. It will enable its users to use it free of cost as Facebook messenger app is free in Bangladesh. People can use it from home or any remote location and can control digital home appliances like fans, lights etc. Smart door locking system, face recognition, gas leakage detection, temperature detection and control, humidity detection, fire detection, which has been done through collecting data from different sensors and passing them to Raspberry PI and then PI passes these to the BOT through a REST API, are the additional features that make our security system safer, effective and easier to use compared to other existing approaches.

Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins

This research paper talks about the development of an innovative IoT-based smart door lock that also uses facial recognition, Internet of Things (IoT) integration, and the ESP32-CAM microcontroller to increase home security. As locks continue to evolve with more new features, the use of IoT is increasing and enhancing the security and usability. ESP32-CAM was selected for this project because of its built-in Wi-Fi connectivity and camera, which render it exceptionally suited for creating a smart lock system that is able to not only recognize the saved faces but also alert the user if there is an intruder. When compared to other microcontrollers like Raspberry Pi, the ESP32- CAM offers many more advantages, including enhanced security features, a userfriendly interface, and a lower cost, and much more in initial price. This paper also compares and talks about already existing facial recognition-based door locks, providing a detailed analysis of their plus points and limitations to understand the current state of the technology. The research also provides a detailed breakdown including the system's development process, selection of components for the device, and compilation of hardware components for the implementation of the model, The paper also includes the algorithms and user interface design. In this paper, you’ll see how each component works, its usage, and how each component works together to ensure reliable and accurate facial recognition. Also, model testing was done to get the device's performance in a number of different conditions and usage areas. The results of these tests show that ESP32-CAM based smart door lock is a good and powerful solution and it is also cost-effective, offering good amount of savings compared to other smart lock systems in the market. This paper highlights how the project talks about some of the common security problems associated with the old locking system. By offering a more accessible and reliable alternative, this smart lock system has the potential to revolutionize home security. The integration of advanced facial recognition technology with IoT makes the home door lock system a more secure and user-friendly approach to home access control.

Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar's test it is not possible to conclude that the differences between predictions are statistically significant. Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy.

The paradigm of computing has completely altered as a result of the development of communication and Information technology. The Internet of Things (IoT) is a type of communication environment made up of web-enabled devices that can communicate, gather, assess, and send data across the network without the need for human participation. The IoT idea is widely established in numerous fields of application, and it has also been used in the medical industry. The Internet of Medical Things (IoMT) refers to the combination of IoT with medical technology. Even though IoMT applications have facilitated real time monitoring in the healthcare sector, it suffers from several security and privacy attacks. The presence of such security and privacy attacks can cause the alteration or disclosure of sensitive data or sometimes authorized users cannot access the data also. Hence, it is crucial to secure the IoMT environment from such malicious attacks. Therefore, this study aims in assessing harmful traffic in IoMT environment. In this research, XGBoost classification algorithm is proposed to classify malicious traffic in an IoMT environment. Further, the study has also implemented state-of-art machine learning algorithms such as DT (Decision Tree), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), NB (Naive Bayes), MLP (Multilayer Perceptron), SGD (Stochastic Gradient Descent) to test the efficacy of the proposed model. The experimental outcomes indicate that the proposed XGBoost algorithm outperformed other conventional machine learning approaches in terms of predicting malicious traffic in an IoMT environment, with a 100-percentage accuracy rate. Furthermore, the XGBoost algorithm exhibits better performance in terms of precision, Fl-score, recall, and AUC as compared with traditional approaches.

Objective: Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Methods: Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis. Results: In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion: Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

A home security system is a need in today's world. However, the usage of keys, security cards, passwords or pattern locks to open most doors has recently been outlawed. Yet robbery and identity theft are much more dangerous repercussions of key loss than the actual loss of a key itself Consequently, this has been deemed a severe problem. A door access control system was successfully installed thanks to the usage of the Internet of Things (IoT). As well as serving as the primary controller for face recognition, youth system, and locking system, the Raspberry Pi is employed as a compact computer board that can be programmed. The guy standing in front of the entryway is captured by a camera on the wall. Whenever the system fails to recognise a person's face, a warning will be sent. Using the Internet of Things, a user may control who has access to a door (IoT). Facial recognition, Rasberry Pi, Internet of things (IOT), and home security systems are some of the terms we'll be using today.