Relevant bibliographies by topics / Mobile activity detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Mobile activity detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Mobile activity detection"

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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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K. Vamshee Krishna, Mithin Reddy Ch, Mallesh Potharaju, and Mahesh Matteri. "EDGE-OPTIMIZED DEEP LEARNING FOR ADAPTIVE MALICIOUS ACTIVITY DETECTION IN MOBILE EDGE NETWORKS." International Journal of Engineering Research and Science & Technology 21, no. 3 (1) (2025): 536–41. https://doi.org/10.62643/ijerst.v21.n3(1).pp536-541.

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Mobile Edge Computing (MEC) is an emerging paradigm designed to enhance the performance of mobile applications by bringing computation and data storage closer to end-users. However, the adoption of MEC has introduced new security challenges, as these systems have become prime targets for malicious activities such as data breaches, denial-of-service (DoS) attacks, and intrusion attempts. These threats exploit infrastructure vulnerabilities, compromising system integrity, availability, and confidentiality. Traditionally, mobile edge security relied on signature-based and rule-based detection met
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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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Dissertations / Theses on the topic "Mobile activity detection"

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Moritz, Rick. "Routine activity extraction from local alignments in mobile phone context data." Phd thesis, INSA de Rouen, 2014. http://tel.archives-ouvertes.fr/tel-00944105.

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Humans are creatures of habit, often developing a routine for their day-to-day life. We propose a way to identify routine as regularities extracted from the context data of mobile phones. We choose Lecroq et al.'s existing state of the art algorithm as basis for a set of modifications that render it suitable for the task. Our approach searches alignments in sequences of n-tuples of context data, which correspond to the user traces of routine activity. Our key enhancements to this algorithm are exploiting the sequential nature of the data an early maximisation approach. We develop a generator o
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Moritz, Rick Patrick Constantin. "Routine activity extraction from local alignments in mobile phone context data." Thesis, Rouen, INSA, 2014. http://www.theses.fr/2014ISAM0001/document.

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L'homme, de manière générale apprécie ses habitudes. Nous proposons une méthodologie d'identification des activités de routine depuis des régularités extraites des données de contexte, acquises sur téléphone portable. Notre choix algorithmique se base sur l'algorithme d'alignement proposé par Lecroq et al. L'algorithme cherche à aligner des séquences de n-uplets de données du contexte. Les séquences algorithmiques correspondent aux traces d'utilisation régulières. Notre contribution technique consiste à l'amélioration de l'algorithme afin qu'il puisse exploiter la nature séquentielle des donné
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Odehnal, Jiří. "Řízení a měření sportovních drilů hlasem/zvuky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399705.

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This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.
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Cappella, Matteo. "Studio e valutazione di tecniche di training per il riconoscimento automatico di attività attraverso dispositivi mobili." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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L'utilizzo degli smartphone è cresciuto rapidamente nel corso dell'ultimo decennio. Questi dispositivi oltre ad avere ottime capacità comunicative, di memoria e di calcolo, sono equipaggiati con numerosi sensori. Quest'ultimi permettono ai ricercatori di raccogliere numerose informazioni riguardanti le persone e il contesto che le circonda. Un aspetto molto importante che è possibile analizzare tramite la raccolta delle informazioni provenienti dai sensori è sicuramente quello del riconoscimento delle modalità di trasporto (Transportation Mode Detection), che consiste, appunto, nell'individuar
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Humphrey, Kevin P. "An actively shielded, adaptively balanced high temperature superconducting quantum interference device ( SQUID ) gradiometer capable of detecting moving targets from a mobile platform." Thesis, University of Strathclyde, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431813.

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ARIYAPALA, DALUWATHUMULLA GAMAGE KANISHKA. "Smartphones, Drones and IoT: Security and Privacy in Heterogeneous Smart Devices." Doctoral thesis, 2017. http://hdl.handle.net/2158/1079068.

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Heterogeneous computing devices are surrounding us in our day-to-day life at an unprecedented rate and they are showing promising capabilities. For example, the drone Loon Copter is one such device providing an unrestricted mobility in air, surface and underwater. Similarly, smartphones and IoT are also enabling to sense our environment. Many of these devices are embedded with processing, sensing, software and communication capabilities, allowing many services to be built on top. In the near future these connected devices will be everywhere from smart cities, factories to our homes and even on
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Book chapters on the topic "Mobile activity detection"

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Thomaz, Edison, Irfan A. Essa, and Gregory D. Abowd. "Challenges and Opportunities in Automated Detection of Eating Activity." In Mobile Health. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51394-2_9.

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Kang, Jaewoong, Jooyeong Kim, Kunyoung Kim, and Mye Sohn. "Complex Activity Recognition Using Polyphonic Sound Event Detection." In Innovative Mobile and Internet Services in Ubiquitous Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93554-6_66.

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Jagat, Rikhi Ram, Dilip Singh Sisodia, and Pradeep Singh. "Semi-Supervised Self-Training Approach for Web Robots Activity Detection in Weblog." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9605-3_64.

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Zhou, Min, Shuangquan Wang, Yiqiang Chen, Zhenyu Chen, and Zhongtang Zhao. "An Activity Transition Based Fall Detection Model on Mobile Devices." In Human Centric Technology and Service in Smart Space. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5086-9_1.

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Fraś, Mariusz, and Mikołaj Bednarz. "Simple Rule-Based Human Activity Detection with Use of Mobile Phone Sensors." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46586-9_4.

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Santos, Tomás Mestre dos, Rui Neto Marinheiro, and Fernando Brito e. Abreu. "Wireless Crowd Detection for Smart Overtourism Mitigation." In Smart Life and Smart Life Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75887-4_11.

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Abstract Overtourism occurs when the number of tourists exceeds the carrying capacity of a destination, leading to negative impacts on the environment, culture, and quality of life for residents. By monitoring overtourism, destination managers can identify areas of concern and implement measures to mitigate the negative impacts of tourism while promoting smarter tourism practices. This can help ensure that tourism benefits both visitors and residents while preserving the natural and cultural resources that make these destinations so appealing. This chapter describes a low-cost approach to moni
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Mustafa, M. K., Tony Allen, and Lindsay Evett. "A Review of Voice Activity Detection Techniques for On-Device Isolated Digit Recognition on Mobile Devices." In Research and Development in Intelligent Systems XXXI. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12069-0_23.

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Novikova, Evgenia, and Igor Kotenko. "Visual Analytics for Detecting Anomalous Activity in Mobile Money Transfer Services." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-10975-6_5.

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Lock, J. C., A. G. Tramontano, S. Ghidoni, and N. Bellotto. "ActiVis: Mobile Object Detection and Active Guidance for People with Visual Impairments." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30645-8_59.

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Al Mulla, Aisha, and Majed Mohamed. "Nongovernmental Organizations’ (NGO) Role in Cancer Care in the UAE: Friends of Cancer Patients as an Example." In Cancer Care in the United Arab Emirates. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6794-0_9.

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AbstractNongovernmental organizations (NGOs) around the world have been promoting cancer care, from prevention to palliation, due to their streamlined bureaucracy and flexible administrative structures. In the United Arab Emirates (UAE), several support groups provide assistance to alleviate the suffering of cancer patients and mobilize resources for medical expenses, advanced treatments, and reintegration into society for cancer survivors.The Sharjah-based Friends of Cancer Patients (FOCP) is one such organization that is actively raising awareness about screening and early detection, advocat
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Conference papers on the topic "Mobile activity detection"

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Yang, Boran, Xiaoxu Zhang, Li Hao, George K. Karagiannidis, and Pingzhi Fan. "Joint Activity Detection and Channel Estimation for MIMO Grant-Free Random Access through Bayesian Learning." In 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2024. https://doi.org/10.1109/pimrc59610.2024.10817353.

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von Rège, H., and W. Sand. "Mini-Plant for Simulation of Metal Corrosion and Biofouling for Evaluation of Countermeasures." In CORROSION 1999. NACE International, 1999. https://doi.org/10.5006/c1999-99306.

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Abstract A mobile mini-plant for the simulation and detection of MIC and biofouling on metals in water circulation systems was developed and tested in laboratory and field experiments. Different metal samples (mild and stainless steel rings and coupons), arranged in ring columns, were tested for their succeptibility against microbiologically influenced corrosion attack (MIC) and biofouling. Sulfur/-compound utilizing bacteria created aggressive conditions for mild, but not for stainless steel (AISI 304). On mild steel, the electrochemical corrosion process is enforced by the continuous availab
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Hasara Pathmaperuma, Madushi, Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "User Mobile App Encrypted Activity Detection." In ESCC '21: The 2nd European Symposium on Computer and Communications. ACM, 2021. http://dx.doi.org/10.1145/3478301.3478303.

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Pham, Cuong, and Nguyen Thi Thanh Thuy. "Real-Time Traffic Activity Detection Using Mobile Devices." In IMCOM '16: The 10th International Conference on Ubiquitous Information Management and Communication. ACM, 2016. http://dx.doi.org/10.1145/2857546.2857611.

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Park, Sangjun, Seunghyung Lee, Jinuk Park, and Minsoo Hahn. "Cluster-based voice activity detection for mobile devices." In 2016 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2016. http://dx.doi.org/10.1109/icce.2016.7430558.

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Wong, Johnny Chun Yiu, Jun Wang, Eugene Yujun Fu, Hong Va Leong, and Grace Ngai. "Activity Recognition and Stress Detection via Wristband." In MoMM2019: The 17th International Conference on Advances in Mobile Computing & Multimedia. ACM, 2019. http://dx.doi.org/10.1145/3365921.3365950.

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Doyle, J., R. Farrell, S. McLoone, T. McCarthy, M. Tahir, and P. Hung. "Utilising mobile phone RSSI metric for human activity detection." In IET Irish Signals and Systems Conference (ISSC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.1728.

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Cherniavsky, Neva, Richard E. Ladner, and Eve A. Riskin. "Activity detection in conversational sign language video for mobile telecommunication." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813363.

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Sharma, Somya, Jabal Raval, and Bhushan Jagyasi. "Neural network based agriculture activity detection using mobile accelerometer sensors." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030539.

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Phatangare, Sheetal, Sumedh Kate, Dipkul Khandelwal, Arya Khandetod, and Aishwarya Kharade. "Real Time Human Activity Detection using YOLOv7." In 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2023. http://dx.doi.org/10.1109/i-smac58438.2023.10290168.

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Reports on the topic "Mobile activity detection"

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Chutimaworapan, Suchada, Chaiyo Chaichantippayuth, and Areerat Laopaksa. Formulation of pharmaceutical products of Garcinia mangostana Linn. extracts. Chulalongkorn University, 2006. https://doi.org/10.58837/chula.res.2006.32.

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Part I: The purpose of the investigation was to develop the extraction process that was simple, practical and giving high yield. The maceration of dried powder of Garcinia mangostana fruit husk with ethyl acetate gave yellow crystalline powder of mangostin. The yield was calculated as 7.47%. The identification of the Garcinia mangostanahusk extract was carried out by thin-layer chromatography (TLC) and differential scanning calorimetry. The TLC of mangostin was done by using the alumina sheet and ethyl acetate: hexane (3:1) as mobile phase. The Rf value as compared with standard mangostin was
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Chamovitz, A. Daniel, and Georg Jander. Genetic and biochemical analysis of glucosinolate breakdown: The effects of indole-3-carbinol on plant physiology and development. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597917.bard.

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Genetic and biochemical analysis of glucosinolate breakdown: The effects of indole-3-carbinol on plant physiology and development Glucosinolates are a class of defense-related secondary metabolites found in all crucifers, including important oilseed and vegetable crops in the Brassica genus and the well-studied model plant Arabidopsis thaliana. Upon tissue damage, such as that provided by insect feeding, glucosinolates are subjected to catalysis and spontaneous degradation to form a variety of breakdown products. These breakdown products typically have a deterrent effect on generalist herbivor
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