Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Using genetic algorithm for process migration in multicore kernels(Springer Verlag service@springer.de, 2017) Shravya, K.S.; Deepak, A.; Chandrasekaran, K.Process migration is used in multicore operating systems to improve their performance. The implementation of the migration event contributes largely to the performance of the scheduling algorithm and hence decides how effective a multicore kernel is. There have been several effective algorithms which decide how a process can be migrated from one core to another in a multicore operating system. This paper looks further into the mechanism of process migration in multicore operating systems. The main aim of this paper is not to answer how the process migration should take place but it aims to answer when process migration should take place and to decide the site of process migration. For this, an artificial intelligence concept called genetic algorithm is used. Genetic algorithm works on the theory of survival of the fittest to find an optimally good solution during decision making phase. © Springer Nature Singapore Pte Ltd. 2017.Item Applications of AI in Health Monitoring of Structures in Potential Seismic Areas—A Review(Springer Science and Business Media Deutschland GmbH, 2022) Ginan, C.P.; Jayalekshmi, B.R.Artificial Intelligence (AI) can be used to solve complex problems in civil engineering which involve time-consuming and arduous tasks, such that, the hurdles that appear when these works are completed using mere human labour can be completely overcome, by employing various techniques of AI. Furthermore, where testing fails or is hardly possible, AI can suffice the required design. AI can be of its best use when applied to the field of Structural Health Monitoring (SHM), which serves to identify and detect the current state and behaviour of structures. This article outlines the applications of AI in SHM in potential seismic zones. SHM functions in seismically prone areas by evaluating on field, the resistive power of a building against earthquakes and simultaneously it's potent to carry forth the services. The paper studies certain observations from research conducted during past few decades on development of artificial intelligence in SHM technologies in seismically intensive areas, in case of multistorey buildings, bridges, special structures and lifeline structures. The article begins with a brief introduction to artificial intelligence, further, detailing applications of AI in SHM in seismically prone areas. Subsequently, the contemporary applications of AI in the field are reviewed, alongside, the adaptability, sufficiency and potentiality of those methods to overcome the barriers of the conventional methods are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Emergence, Evolution, and Applications of Medical Cyber-Physical Systems(Springer Science and Business Media Deutschland GmbH, 2024) Rathore, R.; Bhowmik, B.The speedy advancement of technology has given rise to a new era of engineered systems called cyber-physical systems (CPS), which are redefining lifestyles worldwide through computer, networking, and control technologies. From health care to transportation, CPS has transformed several industries. By bringing about significant improvements in patient care, diagnosis, and treatment, Medical Cyber-Physical Systems (MCPS) transform the healthcare industry. Exploration of the area is, therefore, imperative. The paper discusses the trajectory of MPCS. It covers the development, necessity, and significance of MCPS. Next, we examine the various uses of MCPS in contemporary life. We then look at many issues and current developments in the medical organization. It makes it easier for patients, medical professionals, and equipment to share information effectively, which allows for prompt decision-making and preventative measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data(Institute of Electrical and Electronics Engineers Inc., 2024) Mukeshbhai, A.N.; Annappa, B.; Sachin, D.N.The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requiring the transfer of sensitive patient data from the original institution. Recent research in FL within the healthcare domain has predominantly relied on centralized datasets, which do not represent real-time data heterogeneity and made assumptions by random data splitting to different medical client institutions. Additionally, it may be challenging for a single global model to encompass the diverse characteristics of various healthcare settings accurately. This paper examines the application of Personalized Federated Learning (PFL) in realistic cross-silo healthcare scenarios with federated natural split datasets in different medical client institutions. This paper discusses the experiments conducted on brain segmentation, survival prediction, melanoma classification, and heart disease di-agnosis. Our experiments show that the proposed PFL techniques consistently improve local model performance over standard FL strategies by up to 10% in different medical use cases. © 2024 IEEE.Item The Advancements of Artificial Intelligence in Tourism and Hospitality Industry: A Bibliometric Review(Springer Science and Business Media Deutschland GmbH, 2025) Talawar, A.; Sheena, S.; Alathur, S.Technological advancements have progressed dramatically in recent times, with artificial intelligence (AI) emerging as a disruptive force reshaping various industries worldwide. AI has revolutionized multiple sectors by offering enhanced customer experiences, optimized processes, and personalized services. In the tourism and hospitality (T&H) industry, AI-driven technologies such as chatbots, virtual assistants, conversational service agents, and ChatGPTs allow travelers to plan their journeys and navigate travel complexities. Due to the rapid development and growing interest in AI applications within the T&H domain, the study conducts a bibliometric analysis to investigate the recent advancements of AI research in the T&H field, identify prominent and emerging research themes, and offer future research directions. A total of 343 documents published between 2018 and 2024 were retrieved from the Scopus database and analyzed using the Bibliometrix R tool. The findings reveal a growing trend in AI research in the domain, with journals such as the “International Journal of Contemporary Hospitality Management†and “Annals of Tourism Research,†which have been influential in AI literature. Key keywords identified include robotics, machine learning, chatbots, service robots, ChatGPT, and smart tourism, highlighting AI’s diverse applications. The co-word analysis revealed several themes, such as AI-driven robotics and automation addressing COVID-19 challenges, advanced computing techniques and immersive technologies like virtual reality and augmented reality offering enriched travel experiences. Finally, bibliometric review provides new insights for tourism organizations and researchers, emphasizing the transformative impact of AI advancements in T&H research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Classification of Longitudinal Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors(Springer Science and Business Media Deutschland GmbH, 2025) Arichandran, R.; Krishnakar, H.S.; Kumar, A.; Mohan, M.Driving behavior profiling (DBP) involves evaluating driving patterns to determine a safety score for drivers. Proper classification of driving events increases the accuracy of profiling driving behaviors. Most driving event classification models consider lateral driving events, such as turning and lane changes. In heavy traffic conditions, it is impossible to perform lateral events independent of the position of other vehicles. This research aims to develop a model for classifying longitudinal driving events (acceleration and braking) and nonevents using vehicle response signals. To develop the model, naturalistic driving data were collected using a passenger car on a 19 km road stretch. Vehicle response signals were collected using Inertial Measurement Unit (IMU) sensors fixed on the test vehicle with a frequency of approximately 200 Hz with timestamps. The driver’s pedal operation was also captured with timestamps using a camera to map the ground truth labels with vehicle response signals. The data were collected from 5 drivers, totaling a dataset for approximately 190 km. The start and end times of all 634 events (444 driving events and 190 nonevents) were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (476 events) and test (158 events) datasets. Hidden Markov Model (HMM) algorithm was used to develop classification models for the driving events. The models were developed for various combinations of accelerations using the training dataset. The accuracy of these models was then compared to a test dataset. The models achieved 90.99% and 77.08% accuracy, respectively, in classifying events and nonevents using data from the accelerometer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.