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Journal articles on the topic 'Mobile activity detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Vural, Ickin, and Hein Venter. "Combating Mobile Spam through Botnet Detection using Artificial Immune Systems." JUCS - Journal of Universal Computer Science 18, no. (6) (2012): 750–74. https://doi.org/10.3217/jucs-018-06-0750.

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Malicious software (malware) infects large numbers of mobile devices. Once infected these mobile devices may be involved in many kinds of online criminal activity, including identity theft, unsolicited commercial SMS messages, scams and massive coordinated attacks. Until recently, mobile networks have been relatively isolated from the Internet, so there has been little need to protect them against Botnets. Mobile networks are now well integrated with the internet, so threats on the internet, such as Botnets, have started to migrate to mobile networks. This paper studies the potential threat of

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Sosnovyy, Vladyslav, and Nataliia Lashchevska. "DETECTION OF MALICIOUS ACTIVITY USING A NEURAL NETWORK FOR CONTINUOUS OPERATION." Cybersecurity: Education, Science, Technique 3, no. 23 (2024): 213–24. http://dx.doi.org/10.28925/2663-4023.2024.23.213224.

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This article describes the problem of detecting malicious programs in running systems of users of mobile applications. Because users can download any application on their phone, which over time can pull up additional settings, which can store malicious routines for monitoring both personal life and their personal data, such as logins, passwords, bank data. The detection of such routines is based on dynamic analysis and is formulated as a weakly controlled problem. The article contains an analysis of information on the development of researchers who worked on detection models and methods such a

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Ahn, Junho, and Richard Han. "Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users." Mobile Information Systems 9, no. 2 (2013): 99–122. http://dx.doi.org/10.1155/2013/360243.

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Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as “unusual” for a mobile user when an event is an infrequently occurring one from the user's normal behavio

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Boukhechba, Mehdi, Abdenour Bouzouane, Bruno Bouchard, Charles Gouin-Vallerand, and Sylvain Giroux. "Energy Optimization for Outdoor Activity Recognition." Journal of Sensors 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/6156914.

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The mobile phone is no longer only a communication device, but also a powerful environmental sensing unit that can monitor a user’s ambient context. Mobile users take their devices with them everywhere which increases the availability of persons’ traces. Extracting and analyzing knowledge from these traces represent a strong support for several applications domains, ranging from traffic management to advertisement and social studies. However, the limited battery capacity of mobile devices represents a big hurdle for context detection, no matter how useful the service may be. We present a novel

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Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (2022): 67. http://dx.doi.org/10.3390/fi14020067.

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In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previ

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Yang, Qiang, and Feng Zhao. "Artificial Intelligence on Mobile Devices: An Introduction to the Special Issue." AI Magazine 34, no. 2 (2013): 9. http://dx.doi.org/10.1609/aimag.v34i2.2470.

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This special issue of the AI Magazine is devoted to some exemplar works of AI on mobile devices. It includes four works that range from mobile activity recognition and air quality detection to machine translation and image compression.

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Emish, Mohamed, Zeyad Kelani, Maryam Hassani, and Sean D. Young. "A Mobile Health Application Using Geolocation for Behavioral Activity Tracking." Sensors 23, no. 18 (2023): 7917. http://dx.doi.org/10.3390/s23187917.

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The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual’s physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in mo

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Mamčenko, Jelena, and Regina Kulvietienė. "Data mining process for fraud detection in mobile communication." Lietuvos matematikos rinkinys 44 (December 17, 2004): 332–38. http://dx.doi.org/10.15388/lmr.2004.31698.

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Without dependence from a sort of activity (sale, rendering of services, etc.) the using of data mining methods can bring the certain advantage.Fraud detection methods of data mining can be applied to this problem quite readily. Three important elements of a data mining application/solution are present. These are the ability to handle large amounts of data, suitable methods and algorithms, and the availability of domain expertise.

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Srujan, Kaluva. "Mobile Camera Application to Monitor Residential Society Vehicle Activity." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40860.

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This project presents a machine learning-enabled surveillance system designed for real-time monitoring and tracking of vehicles in urban and residential environments. With rapid advancements in autonomous technologies, there is an increasing demand for intelligent systems that can enhance public safety, streamline traffic management, and provide secure access control within private and public spaces. The proposed system leverages deep learning algorithms for vehicle detection, classification, and speed monitoring, while utilizing IoT infrastructure to enable seamless data collection and remote

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Matzliach, Barouch, Irad Ben-Gal, and Evgeny Kagan. "Cooperative Detection of Multiple Targets by the Group of Mobile Agents." Entropy 22, no. 5 (2020): 512. http://dx.doi.org/10.3390/e22050512.

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The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The goal is to define the trajectories of the agents that can lead to the detection of the targets in minimal time. The suggested solution follows the classical Koopman’s approach applied to an occupancy grid, while the decision-making and control schemes are conducted based on information-theoretic criteria. Sensor fusion in each agent and over the a

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Karthikayen, A., and Selvakumar S. Raja. "Bates Distribution Inspired Trust Factor-Based Selfish Node Detection Technique in Mobile Adhoc NETworks." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 609–15. http://dx.doi.org/10.1166/jctn.2019.7778.

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The trustworthiness of the mobile nodes is considered as the predominant parameter for ensuring significant data dissemination in the ad hoc network. However, the selfishness activity of the mobile nodes minimizes the trust of the mobile nodes by dropping a considerable number of data packets in the network. The significant dropping of data packets by the selfish node introduces huge data overhead with increased latency and energy consumptions by increasing the number of retransmissions. In this paper, a Bates Distribution Inspired Trust Factor-based Selfish Node Detection Technique (BDITF-SND

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Aloni, Sukhada, and Divya Shekhawata. "Detection of suspicious activity using mobile sensor data and Modified Sub-space K-NN for criminal investigations." YMER Digital 21, no. 08 (2022): 578–91. http://dx.doi.org/10.37896/ymer21.08/49.

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With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. We expect the

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Fitriah, Fitriah, and Mustofa Haris. "IMPLEMENTASI KADER YOUNG MOBILE POSBINDU UPAYA PROMOTIF DAN PREVENTIF PENYAKIT TIDAK MENULAR." JURNAL PARADIGMA (PEMBERDAYAAN & PENGABDIAN KEPADA MASYARAKAT) 3, no. 2 (2021): 45–53. http://dx.doi.org/10.36089/pgm.v3i2.584.

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The implementation of this community service aims to increase promotive and preventive activities in an effort to prevent non-communicable diseases through PTM young mobile posbindu activities. Previously, PTM Posbindu activities in Rosep Village had not run optimally with low visits during activities. Meanwhile, in Rosep Village, hypertension cases are very high. This community service activity is carried out in September - October 2021. The focus of this community service activity is to increase posbindu activities with a walking posbindu approach with local village youth as cadres. Teenager

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Viet, Vo Quang, Ali Fahmi Perwira Negera, Hoang Minh Thang, and Deokjai Choi. "Energy Saving in Forward Fall Detection using Mobile Accelerometer." International Journal of Distributed Systems and Technologies 4, no. 1 (2013): 78–94. http://dx.doi.org/10.4018/jdst.2013010106.

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Fall injury is one of the biggest risks to health and well-being of the elderly especially in independent living because falling accidents may cause instant death. There are many research interests aimed to detect fall incidents. Fall detection is envisioned critical on ICT-assisted healthcare future. In addition, mobile battery is currently another serious problem in which performance feasibility is considered as a standard to verify an effective method. In this paper, the authors study forward fall detection method from mobile phone perspective using accelerometer only without sacrificing ac

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Hussain, Ibrar, and Muhammad Asif. "Detection of Anomalous Transactions in Mobile Payment Systems." International Journal of Data Analytics 1, no. 2 (2020): 58–66. http://dx.doi.org/10.4018/ijda.2020070105.

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Mobile payment systems are providing an opportunity for smartphone users for transferring money to each other with ease. This simple way of transferring through mobile payment systems has great potential for economic activity. However, fraudulent transactions may occur and can have a substantial impact on the economy of a country. Financial fraud and anomalous transactions can cause a loss of billions of dollars annually. Therefore, there is a need to detect anomalous transactions through mobile payment systems to prevent financial fraud. For this research study, a synthetic dataset is generat

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Novikova, Evgenia Sergeevna, and Igor Vitalievich Kotenko. "Detection of Anomalous Activity in Mobile Money Transfer Services Using RadViz-Visualization." SPIIRAS Proceedings 5, no. 48 (2016): 32. http://dx.doi.org/10.15622/sp.48.2.

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Wu, Bian, Xiaolin Ren, Chongqing Liu, and Yaxin Zhang. "A Robust, Real-Time Voice Activity Detection Algorithm for Embedded Mobile Devices." International Journal of Speech Technology 8, no. 2 (2005): 133–46. http://dx.doi.org/10.1007/s10772-005-2165-7.

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Wu, Bian, Xiaolin Ren, Chongqing Liu, and Yaxin Zhang. "A Robust, Real-Time Voice Activity Detection Algorithm for Embedded Mobile Devices." Journal of Sol-Gel Science and Technology 8, no. 2 (1997): 133–46. http://dx.doi.org/10.1007/s10971-005-2165-8.

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Novikova, Evgenia, Igor Kotenko, and Evgenii Fedotov. "Interactive Multi-View Visualization for Fraud Detection in Mobile Money Transfer Services." International Journal of Mobile Computing and Multimedia Communications 6, no. 4 (2014): 73–97. http://dx.doi.org/10.4018/ijmcmc.2014100105.

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Mobile money transfer services (MMTS) have gained a solid market segment and are widely used for domestic and international money transfers. Like traditional financial systems they can be used to conduct illegal financial activity including money laundering or usage of malicious software to gain access to mobile money. The paper considers an interactive multi-view approach for detection of the fraudulent activity in the MMTS. It considers a set of visualization techniques enabling comprehensive analysis of the behavior of the MMTS subscriber according to his/her transaction activity. The autho

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Adepu, Ashwin. "Security Risks and Mitigation Strategies in Mobile Banking Applications." International Journal of Innovative Research in Information Security 10, no. 05 (2024): 654–59. http://dx.doi.org/10.26562/ijiris.2024.v1005.01.

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Financial services have undergone a change thanks to mobile banking apps, which give consumers quick and easy access to their accounts. Significant security risks, such as phishing attacks, data breaches, and unauthorized access, are associated with this convenience. In order to assess mitigation measures, this study employs a variety of algorithms to explore major security concerns related to mobile banking applications. Intrusion detection and data encryption are our two main areas of concentration.We evaluate the capability of Convolutional Neural Networks (CNN) and Support Vector Machines

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Wang, Yadi, Wangyang Yu, Peng Teng, Guanjun Liu, and Dongming Xiang. "A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking." Wireless Communications and Mobile Computing 2022 (January 4, 2022): 1–14. http://dx.doi.org/10.1155/2022/4434714.

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With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data

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Kannan, Aieswarya, and Abbas Z. Kouzani. "Violence Detection Using Wi-Fi and 5G/6G Sensing Technologies: A Review." Electronics 13, no. 14 (2024): 2765. http://dx.doi.org/10.3390/electronics13142765.

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Violence, a pervasive societal concern, demands innovative approaches for its early detection and prevention. This review paper explores the intersection of violence detection and wireless fidelity (Wi-Fi), alongside fifth-generation (5G) and sixth-generation (6G) mobile technologies. Wi-Fi sensing, initially employed for human activity detection, has also demonstrated versatility across a number of other important applications. The significance of leveraging Wi-Fi sensing for violence detection is investigated, underscoring its ability to enhance security protocols and minimise response time.

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Bouchard-Roy, Jacob, Aidin Delnavaz, and Jérémie Voix. "Mobile In-Ear Power Sensor for Jaw Joint Activity." Micromachines 11, no. 12 (2020): 1047. http://dx.doi.org/10.3390/mi11121047.

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In only a short time, in-ear wearables have gone from hearing aids to a host of electronic devices such as wireless earbuds and digital earplugs. To operate, these devices rely exclusively on batteries, which are not only cumbersome but known for several drawbacks. In this paper, the earcanal dynamic movements generated by jaw activity are evaluated as an alternative source of energy that could replace batteries. A mobile in-ear power sensor device capable of measuring jaw activity metrics is prototyped and tested on three test subjects. The test results are subsequently analyzed using a detec

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Zakaria, Benhaili, Balouki Youssef, and Moumoun Lahcen. "Detecting human fall using internet of things devices for healthcare applications." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 561–69. https://doi.org/10.11591/ijai.v14.i1.pp561-569.

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Falls pose a significant threat to unintentional injuries, particularly impacting the independence of older individuals. Existing detection methods suffer from drawbacks, including inaccuracies, wearer discomfort, complex setup, resource-intensive computation, and limitations in detecting falls outside a specific setting. In response, our innovative fall detection system integrates with a pneumatic solution, analyzing fundamental human activities like running, walking, and sitting, both indoors and outdoors. This approach combines wearable sensors with a vision-based solution, utilizing a smar

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Byrne, Simon, Beth Kotze, Fabio Ramos, Achim Casties, and Anthony Harris. "Using a mobile health device to manage severe mental illness in the community: What is the potential and what are the challenges?" Australian & New Zealand Journal of Psychiatry 54, no. 10 (2020): 964–69. http://dx.doi.org/10.1177/0004867420945782.

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There has been a revolution in the use of mobile health devices for monitoring physical health. There is more recent interest in whether these devices can also be used for monitoring symptoms of mental illness. This paper considers how stress increases risk of mental deterioration and individuals with mental illness are sensitive to the effects of stress. It discusses how an inexpensive mobile health device could be used for detecting physiological signs of stress: deviations in biometrics such as sleep, activity and arousal may reflect a stress response and increased risk of relapse. These bi

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Neumann, Thea, Maren Krüger, Jasmin Weisemann, et al. "Innovative and Highly Sensitive Detection of Clostridium perfringens Enterotoxin Based on Receptor Interaction and Monoclonal Antibodies." Toxins 13, no. 4 (2021): 266. http://dx.doi.org/10.3390/toxins13040266.

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Clostridium perfringens enterotoxin (CPE) regularly causes food poisoning and antibiotic-associated diarrhea; therefore, reliable toxin detection is crucial. To this aim, we explored stationary and mobile strategies to detect CPE either exclusively by monoclonal antibodies (mAbs) or, alternatively, by toxin-enrichment via the cellular receptor of CPE, claudin-4, and mAb detection. Among the newly generated mAbs, we identified nine CPE-specific mAbs targeting five distinct epitopes, among them mAbs recognizing CPE bound to claudin-4 or neutralizing CPE activity in vitro. In surface plasmon reso

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Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "Deep Learning for Encrypted Traffic Classification and Unknown Data Detection." Sensors 22, no. 19 (2022): 7643. http://dx.doi.org/10.3390/s22197643.

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Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from

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Sara, Meghshanth. "Stress Detection Smartwatch." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3796–802. http://dx.doi.org/10.22214/ijraset.2022.45865.

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Abstract: When a person is unable to handle their circumstances, responsibilities, and workload, stress is a natural emotion that is produced. A person's physical and mental health may suffer when the body is triggered, which can be deadly. The physical impacts of stress on a person's body can include an increase in blood pressure, a rapid heartbeat, increased muscle tension, headaches, a decrease in bodily immunity functions, and a decrease in sleepiness, among other things. The latest technology, known as smartwatches, provides the user with easy access to mobile features. Users can employ t

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L, Latha, Cynthia J, G. Seetha Lakshmi, Raajshre B, Senthil J, and Vikashini S. "Human Activity Recognition Using Smartphone Sensors." Webology 18, no. 04 (2021): 1499–511. http://dx.doi.org/10.14704/web/v18si04/web18294.

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In today’s digitalized world, smartphones are the devices which have become a basic and fundamental part of our life. Since, these greatest technology’s appearance, an uprising has been created in the industry of mobile communication. These greatest inventions of mankind are not just constricted for calling these days. As the capabilities and the number of smartphone users increase day by day, smartphones are loaded with various types of sensors which captures each and every moment, activities of our daily life. Two of such sensors are Accelerometer and Gyroscope which measures the acceleratio

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Sadoun, Maria Sara Nour, Juan Manuel Vargas, Mohamed Mouad Boularas, Arnaud Boutin, François Cottin, and Taous-Meriem Laleg-Kirati. "Cognitive Stress Detection during Physical Activity using Simultaneous, Mobile EEG and ECG signals." IFAC-PapersOnLine 58, no. 24 (2024): 291–96. http://dx.doi.org/10.1016/j.ifacol.2024.11.052.

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Álvarez de la Concepción, Miguel Ángel, Luis Miguel Soria Morillo, Juan Antonio Álvarez García, and Luis González-Abril. "Mobile activity recognition and fall detection system for elderly people using Ameva algorithm." Pervasive and Mobile Computing 34 (January 2017): 3–13. http://dx.doi.org/10.1016/j.pmcj.2016.05.002.

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Moreno, Francisco, Silvia Uribe, Federico Alvarez, and Jose Manuel Menendez. "Extending Aspect-Oriented Programming for Dynamic User's Activity Detection in Mobile App Analytics." IEEE Consumer Electronics Magazine 9, no. 2 (2020): 57–63. http://dx.doi.org/10.1109/mce.2019.2953738.

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K., N. Apinaya Prethi, Sangeetha M., Nithya S., Priyadharshini G., and Anithadevi N. "An Electric Eye for Human Activity Recognition: A Hybrid Neural Network." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2806–9. https://doi.org/10.35940/ijeat.C5957.029320.

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A real time detection of human movements is a practical solution to monitor aged people or mentally challenged people with the permission of their family. Household person is needed to monitor the elder and differently abled people. Instead of monitoring their activities with the help of other people, smart phones are used as a remote to monitor their activities and simultaneously send the message to their family members. The accelerometer sensor placed in the mobile phones. It is used to identify the activities of the person who holds the mobile phones. The most commonly used classifier techn

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Dhivya, Karunya S., and Kumar Krishna. "Human Activity Recognition Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1024–28. https://doi.org/10.35940/ijeat.E9771.069520.

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Human action in a video based application plays a significant role that alerts the researchers towards recognizing the motion of human. Other video applications also have video content extraction, summarization, and human computer interactions. The existing methods needs manual footnote of pertinent portion of actions of our interest. Recognition of human action can be done authentic without physical commentary of applicable parts of action of any one’s interest. In this paper we try to update the previous reviews on many ways of recognizing Human activities in videos that had different

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Rajalakshmi, D., and K. Meena. "A Hybrid Intrusion Detection System for Mobile Adhoc Networks using FBID Protocol." Scalable Computing: Practice and Experience 21, no. 1 (2020): 137–45. http://dx.doi.org/10.12694/scpe.v21i1.1642.

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The security in a mobile ad hoc networks is more vulnerable and susceptible to the environment, because in this network no centralized environment for monitoring individual nodes activity during communication. The intruders are hacked the networks either locally and globally. Now a day’s mobile ad hoc network is an emerging area of research due to its unique characteristics. It’s more vulnerable to detect malicious activities, and error prone in nature due to their dynamic topology configuration. Based on their difficulties of intrusion detection system, in this paper proposed a novel approach

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Aloni, Sukhada, and Divya Shekhawat. "Detection of Suspicious Activity using Mobile Sensor Data and Modified Sub-space K-NN for Criminal Investigations." Journal of Advanced Zoology 44, S7 (2023): 130–37. http://dx.doi.org/10.17762/jaz.v44is7.2743.

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With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. This study lay

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Basan, Elena, Alexandr Basan, and Alexey Nekrasov. "Method for Detecting Abnormal Activity in a Group of Mobile Robots." Sensors 19, no. 18 (2019): 4007. http://dx.doi.org/10.3390/s19184007.

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The range of attacks implemented on wireless networks is quite wide. To avoid or reduce the likelihood of an attack, it is necessary to use various defense mechanisms. Existing protection mechanisms are not always suitable for robotic systems and may not fully provide the necessary level of security. Thus, it is necessary to develop new ways of protection, which would be specific to groups of mobile robots. In this study, we propose an analysis of the following cyber parameters: the power consumption and the residual energy, as well as an in-depth traffic analysis to evaluate the effectiveness

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Esmaeili Kelishomi, Aghil, A. H. S. Garmabaki, Mahdi Bahaghighat, and Jianmin Dong. "Mobile User Indoor-Outdoor Detection Through Physical Daily Activities." Sensors 19, no. 3 (2019): 511. http://dx.doi.org/10.3390/s19030511.

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An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a

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Park, Soojin, Sungyong Park, and Kyeongwook Ma. "An Automatic User Activity Analysis Method for Discovering Latent Requirements: Usability Issue Detection on Mobile Applications." Sensors 18, no. 9 (2018): 2963. http://dx.doi.org/10.3390/s18092963.

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Starting with the Internet of Things (IoT), new forms of system operation concepts have emerged to provide creative services through collaborations among autonomic devices. Following these paradigmatic changes, the ability of each participating system to automatically diagnose the degree of quality it is providing is inevitable. This paper proposed a method to automatically detect symptoms that hinder certain quality attributes. The method consisted of three steps: (1) extracting information from real usage logs and automatically generating an activity model from the captured information; (2)

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Narziev, Nematjon, Hwarang Goh, Kobiljon Toshnazarov, Seung Ah Lee, Kyong-Mee Chung, and Youngtae Noh. "STDD: Short-Term Depression Detection with Passive Sensing." Sensors 20, no. 5 (2020): 1396. http://dx.doi.org/10.3390/s20051396.

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It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and foo

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Valiveti, Sharada Ramakrishna, Anush Manglani, and Tadrush Desai. "Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks." International Journal of Systems and Software Security and Protection 12, no. 2 (2021): 11–32. http://dx.doi.org/10.4018/ijsssp.2021070102.

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Ad hoc networks are used in heterogeneous environments like tactical military applications, where no centrally coordinated infrastructure is available. The network is required to perform self-configuration, dynamic topology management, and ensure the self-sustainability of the network. Security is hence of paramount importance. Anomaly-based intrusion detection system (IDS) is a distributed activity carried out by all nodes of the network in a cooperative manner along with other related network activities like routing, etc. Machine learning and its advances have found a promising place in anom

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Yang, Taehun, Sang-Hoon Lee, and Soochang Park. "AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments." Electronics 10, no. 19 (2021): 2374. http://dx.doi.org/10.3390/electronics10192374.

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Recently, many disasters have occurred in indoor places. In order to rescue or detect victims within disaster scenes, vital information regarding their existence and location is needed. To provide such information, some studies simply employ indoor positioning systems to identify each mobile device of victims. However, their schemes may be unreliable, since people sometimes drop their mobile devices or put them on a desk. In other words, their methods may find a mobile device, not a victim. To solve this problem, this paper proposes a novel individual monitoring system based on edge intelligen

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Sachin Y. Meshram and K. Nagaraju. "Accident detection and alert system using global system for mobile communication." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 648–54. https://doi.org/10.30574/wjaets.2024.13.2.0646.

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As vehicles getting to be progressively reasonable, there has been a surge in the number of vehicles on the streets on an normal all over the world. The quick advancement of innovation and framework has made our lives simpler these days. The start of innovation has moreover expanded the activity dangers and street mishaps take put routinely, which causes gigantic misfortune of life and property since of the destitute crisis offices. Mischances bring demolition upon casualties, causing the misfortune of valuable time and cash. It has been set up, after broad inquire about, that a lion's share o

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Bilton, Kyle J., Tenzing H. Y. Joshi, Mark S. Bandstra, Joseph C. Curtis, Daniel Hellfeld, and Kai Vetter. "Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection." Journal of Nuclear Engineering 2, no. 2 (2021): 190–206. http://dx.doi.org/10.3390/jne2020018.

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Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and t

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Amouri, Amar, Vishwa T. Alaparthy, and Salvatore D. Morgera. "A Machine Learning Based Intrusion Detection System for Mobile Internet of Things." Sensors 20, no. 2 (2020): 461. http://dx.doi.org/10.3390/s20020461.

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Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these netw

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Koctúrová, Marianna, and Jozef Juhár. "A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI." Applied Sciences 11, no. 2 (2021): 674. http://dx.doi.org/10.3390/app11020674.

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With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The nove

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Si, Xia-Meng. "Research on System Structure of Mobile Internet Security Audit." International Journal of Interdisciplinary Telecommunications and Networking 8, no. 2 (2016): 12–20. http://dx.doi.org/10.4018/ijitn.2016040102.

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As the development of mobile internet bring convenient for people, the openness and variety of services make its security issues more complicated than those of traditional network. Firewall and intrusion detection focuses on external aggression, and cannot prevent revealing of internal information. As supplementary, security audit technology can monitor internal users' activity, forbid abnormal behavior of internal users. The author introduces related works about mobile internet security audit, comb through matured products on the market, and analyze current security status and architecture of

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Can, Yekta Said, Heather Iles-Smith, Niaz Chalabianloo, et al. "How to Relax in Stressful Situations: A Smart Stress Reduction System." Healthcare 8, no. 2 (2020): 100. http://dx.doi.org/10.3390/healthcare8020100.

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Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual’s health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the m

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Can, Yekta Said, Heather Iles-Smith, Niaz Chalabianloo, et al. "How to Relax in Stressful Situations: A Smart Stress Reduction System." Healthcare 8, no. 2 (2020): 147–63. https://doi.org/10.5281/zenodo.4293918.

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Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests t

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Al-Naji, Ali, Ali J. Al-Askery, Sadik Kamel Gharghan, and Javaan Chahl. "A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption." Journal of Sensor and Actuator Networks 8, no. 2 (2019): 32. http://dx.doi.org/10.3390/jsan8020032.

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Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microco

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