Enhancing Healthcare Outcomes by Real-Time Data Collecting and Predictive Analytics: Implementing IoHT to Remote Patient Monitoring D. Karunkuzhali, M. Ayeesha Nasreen, D Shobana, Vijay Vasanth Aroulanandam, M Tamilselvi, et al. Proceedings of International Conference on Emerging Technologies and Innovation for Sustainability Emergin 2024, 2024 Innovative healthcare solutions have been made possible by the fast growth of technology, especially with the introduction of the Internet of Healthcare Things (IoHT). With a focus on remote patient monitoring, this study explores how the Internet of Health Things (IoHT) could improve healthcare outcomes through the use of predictive analytics and real-time data collecting. With the rise of chronic diseases, inaccessible healthcare, and problems with patient management, the Internet of Health Things (IoHT) presents a revolutionary solution to these problems through the use of wearable sensors, smart medical devices, and mobile health apps. The significance of real-time data collection, which allows healthcare practitioners to remotely acquire health-related information, check vital signs, and monitor patients continually, is highlighted in this paper. Healthcare providers may anticipate their patients' needs, spot any dangers, and take preventative measures by using cutting-edge data analytics tools like machine learning and artificial intelligence. Timely interventions can greatly improve patient outcomes and decrease healthcare costs; this is especially true in the management of chronic illnesses like diabetes and cardiovascular diseases, which the research demonstrates in a number of use cases of IoHT. The research goes on to discuss the difficulties of deploying IoHT solutions, such as privacy and data security issues, device compatibility, and the necessity of a solid healthcare infrastructure. The article goes on to talk about how regulatory frameworks play a part in making sure IoHT technologies are deployed safely and effectively. In order to improve the quality of treatment patients receive, the research seeks to solve these obstacles and offer a complete framework for integrating IoHT into current healthcare systems. This research enhances our understanding of IoHT and its effects on healthcare delivery by reviewing the literature, conducting case studies, and analyzing current trends. Improved patient engagement and treatment plan adherence, as well as better health outcomes, can be achieved by healthcare providers through the use of real-time data and predictive analytics. This opens the door to a more responsive, patient-centered, and efficient healthcare system.
Enhancing Road Safety and User Experience through Intelligent Tollgate Systems and Vehicular Communication Networks Bharath Singh Jebaraj, M.Ayeesha Nasreen, Sushmitha S, Azarudeen K, T.D. Subha, et al. Proceedings 2024 5th International Conference on Image Processing and Capsule Networks Icipcn 2024, 2024 The automotive industry’s growth, fueled by technology and job opportunities, has heightened global automobile sales. Despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside Communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. Leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. This study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field.
Long short-term memory-based power-aware algorithm for prompt heterogenous activity M Ayeesha Nasreen, Selvi Ravindran International Journal of Communication Systems, 2022 SummaryHuman–computer interaction plays a vital role in wireless body area networks, internet of things, and big data. Wearables are low‐power devices with minimal battery capacity. In general, wearables suffer from energy losses due to changes in the user's body posture, diffraction, reflection, and shadowing of the human body. As a result, many control packets are needed to ensure proper communication in wireless body area network. Hence, this research proposes a long short‐term memory‐based power‐aware (LSTM‐PA) algorithm to ensure burst data transmission during prompt heterogeneous activities in the presence and absence of inter‐WBAN interference. The proposed algorithm predicts the best quality time (BQT) for data transmission by activity classification and robust R2 similarity (RRS) metric. The minimum transmission power is estimated by the critical point classification technique. The activity classification accuracy is 92% in the LSTM‐PA algorithm. The energy consumption in the node is reduced by up to 46.34% compared to benchmark algorithms.
An Overview of Q-learning based Energy Efficient Power Allocation in WBAN(Q-EEPA) M. Ayeesha Nasreen, Selvi Ravindran Proceedings 2022 2nd International Conference on Innovative Sustainable Computational Technologies Cisct 2022, 2022 Energy-efficient routing is one of the key techniques to design an efficient sensor network. In Wireless Body Area Networks (WBAN), Transmission Power Control algorithms, interference mitigation algorithms, and efficient routing schemes helps to save energy. The proposed Q-learning-based energy-efficient power allocation (Q-EEPA) provides a high packet delivery ratio with minimum power level, mitigating interference, and minimum hop count. This work considers the design issues in tier 1 and tier 2 of WBAN architecture and gave an overview to solve the issues using the Q-learning algorithm. Q-learning algorithm will allocate the power and routing path during data transmission. Heterogenous activities of a wearer and extensive game theory algorithm act as input to the Q-learning model to choose transmission power level and the next hop in both interference and non-interference scenarios.
Predicting Melancholy risk among IT professionals using Modified Deep Learning Neural Network (MDLNN) S. Rosaline, M. Ayeesha Nasreen, P. Suganthi, T. Manimegalai, G. Ramkumar Proceedings 2022 IEEE 11th International Conference on Communication Systems and Network Technologies Csnt 2022, 2022 Stress disorders are a widespread problem among IT workers who are now employed in the business. Changing lifestyles and workplace cultures, according to study, increase the likelihood of employees experiencing stress at their jobs. However, regardless of the fact that many companies and sectors provide mental wellbeing system and make attempts to enhance the workplace environment, the problem is far from under control. It is our goal in this paper to employ modified deep learning neural network (MDLNN) approaches to assess stress patterns in IT professionals and to identify the components that are most strongly associated with stress levels. It was decided to use data from the OSMI mental health survey 2017, which included responses from working professionals in the technology industry, to achieve this goal. After thorough data cleaning and preparation, we used a modified deep learning neural network to train our model. The correctness of the models mentioned above was determined and compared to one another. Gender, history of family, and the availability of health benefits in the employment were found to be the most significant factors influencing stress, according to the DLMNN model. Industries may now refine their strategy to stress reduction and create a far more comfortable work environment for their employees as a result of the findings of this study.
Data Transmission Dielectric Recombination Proving Ground Besides Bilateral Interaction Sites Software Architecture T.D. Subha, S. Selvalakshmi, M.Ayeesha Nasreen, M. Logeshwaran, Dinesh Anton Raja.P, et al. Proceedings of 5th International Conference on Contemporary Computing and Informatics Ic3i 2022, 2022 The main of this work is Interaction mostly within system as a new significant research topic that will help to advance customized treatment by permitting significant surveillance in individual. For this work, data is transmitted with based on inter wires using the resources electrochemical reactions coefficient technique. The best characteristics of such an innovative acoustic debit Liquid chromatographic routing protocol seem to be reduced maintenance specifications unless this one also hardly combines multiple standard PCs of advanced audio endorse as well as Matlab software, able to empower because most everything of this same specifications can be constantly updated toward the Workstation system preferences and Matlab programs, significant hemodynamic available bandwidth, but instead nearly error-free interaction besides constructing unique genetic gradient methodologies. Thus, in proposed technique, something that is performed out as well as evaluated using authentic chicken material. A channel over interference proportion computation has been established, where could be exploited for a measurement system to determine the design process including phase misalignment reimbursement. The established GC testbed can simply be copied by interested researchers to conduct simulation-based experiments, stimulating fresh research in the field. Furthermore, the proposed GC transceiver's Matlab source code is publicly available on Code Ocean.
