Browsing Faculty Contributions by Subject "Machine learning"
Now showing items 41-60 of 139
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Deep learning based identification of DDoS attacks in industrial application
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Conference Paper)Denial of Service (DoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets dropping them before passing through is the best prevention ... -
Design and implementation of programmable multi-parametric 4-degrees of freedom seismic waves ground motion simulation IoT platform
( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)The early warning and disaster management agencies spend billions of dollars to counter and cater earthquakes but it has always been unique accident. In this work, a programmable four degrees of freedom electromechanical ... -
Detecting anomalies within smart buildings using do-it-yourself internet of things
( Springer , 2022 , Article)Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We ... -
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
( Elsevier , 2022 , Article Review)As the globally increasing population drives rapid urbanization in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace ... -
Development of a stacked machine learning model to compute the capability of ZnO-based sensors for hydrogen detection
( Elsevier , 2024 , Article)Zinc oxide (ZnO) nanocomposite sensors decorated with various dopants are popular tools for detecting even low hydrogen (H2) concentrations. The nanocomposite's chemistry, temperature, and H2 concentration impact the success ... -
Development of oil formation volume factor model using adaptive neuro-fuzzy inference systems ANFIS
( Society of Petroleum Engineers , 2021 , Conference Paper)The oil formation volume factor is one of the main reservoir fluid properties that plays a crucial role in designing successful field development planning and oil and gas production optimization. The oil formation volume ... -
DLRT: Deep learning approach for reliable diabetic treatment
( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin ... -
An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning
( Springer , 2021 , Article)COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce ... -
Editorial: Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC)
( Springer Science and Business Media Deutschland GmbH , 2022 , Conference Paper)Machine learning (ML) is the subcategory of artificial intelligence (AI), which has the capability to imitate human behavior intelligently as per the task performed by the human. In the modern time, any organization ... -
Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
( Elsevier , 2022 , Article)We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ... -
Energy-Aware Distributed Edge ML for mHealth Applications with Strict Latency Requirements
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)Edge machine learning (Edge ML) is expected to serve as a key enabler for real-time mobile health (mHealth) applications. However, its reliability is governed by the limited energy and computing resources of user equipment ... -
Enhancing Safety in Geological Carbon Sequestration: Supervised Machine Learning for Early Detection and Mitigation of CO2 Leakage in Injection Wells
( International Petroleum Technology Conference (IPTC) , 2024 , Conference Paper)The efficient and safe operation of CO2 injection wells during geological sequestration is crucial for successful carbon capture and storage (CCS) projects. This study explores the application of machine learning in creating ... -
Enhancing vulnerability assessment through spatially explicit modeling of mountain social-ecological systems exposed to multiple environmental hazards
( Elsevier , 2024 , Article)The evaluation of the vulnerability of coupled socio-ecological systems is critical for addressing and preventing the adverse impacts of various environmental hazards and devising strategies for climate change adaptation. ... -
Estimating Blood Glucose Levels Using Machine Learning Models with Non-Invasive Wearable Device Data
( IOS Press BV , 2023 , Conference Paper)In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ... -
Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques
( MDPI AG , 2020 , Article)Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; ... -
Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions
( Elsevier , 2023 , Article)Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital ... -
Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
( Elsevier , 2022 , Article)This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of ... -
Exploration and analysis of On-Surface and In-Air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods
( Elsevier , 2023 , Article)Dysgraphia is a type of learning disorder that affects children’s writing skills. Poor writing skills can obstruct students’ academic growth if it is undiagnosed and untreated properly in the early stages. The irregularity ... -
Factors Affecting Student Satisfaction Towards Online Teaching: A Machine Learning Approach
( Springer Science and Business Media Deutschland GmbH , 2022 , Conference Paper)During the outbreak of the Covid-19 pandemic, universities were forced to adopt technology and collaboration tools to reinforce online teaching and sustain their operations. This radical change pushes universities, ... -
FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model
( Elsevier , 2022 , Article)Fiber-reinforced polymer (FRP) composites have recently been considered in the field of structural engineering as one of the best alternatives to conventional steel reinforcement due to their high tensile strength, ...