In Intelligent Transportation systems (ITS), Artificial Intelligence (AI) has transformed transportation network management. This paper explores driver identification using machine learning to enhance vehicle security...
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ISBN:
(数字)9798350379587
ISBN:
(纸本)9798350379594
In Intelligent Transportation systems (ITS), Artificial Intelligence (AI) has transformed transportation network management. This paper explores driver identification using machine learning to enhance vehicle security and efficiency. AI applications in ITS enable real-time data processing, predictive maintenance, and better traffic management, promoting a safer and more efficient ecosystem. Driver identification, or finger-printing, utilizes machine learning to identify drivers based on their unique driving behaviors, helping prevent unauthorized vehicle use, reducing theft, ensuring regulatory compliance, and optimizing resource allocation. Our research employs the AdaBoost algorithm, leveraging OBD- II data such as vehicle speed, RPM, coolant temperature, torque, accelerometer, and GPS for accurate driver identification with an accuracy of 96%. This approach capitalizes on AdaBoost's real-time processing capability, enhancing prediction accuracy, reducing overfitting, and improving generalization to new data. The results highlight the significant potential of AI -driven methods to advance ITS capabilities.
Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusio...
Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.
This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model...
This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model aims to provide accurate predictions of recession occurrence, utilizing historical data and rigorous cross-validation techniques. The model's significance lies in its potential applications in financial prudence and risk management, enabling job seekers to plan their personal finances and career trajectories, while companies can integrate these insights into strategic planning and supply chain optimization. The model's interpretability provides actionable insights by highlighting the significance and direction of each variable's impact on the predicted recession rate. The paper contributes to a deeper understanding of economic recessions and empowers stakeholders to navigate challenges with resilience and confidence. The model's deployment offers a practical tool for individuals and organizations to make informed choices during economic uncertainties, ultimately fostering prudent and sustainable economic strategies.
Rapid point-of-care (POC) assessment of thrombosis is clinically important in patients who develop significant blood coagulation abnormalities such as noted with sepsis or COVID-19. In this work, we compare the coagul...
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Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban ai...
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Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban airflow environment is to design a robust inner loop flight controller. This study investigates the effect of filtered Angular Random Walk (ARW) error found in Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain stable, wings level, horizontal flight, while not causing noticeable discomfort to passengers and respecting the limits of authority of the aircraft's control surfaces in a representative urban airflow environment. The performance of two controller architectures were investigated: classical Proportional, Integral, Derivative (PID) control scheme as well as Linear Active Disturbance Rejection Control (LADRC) control scheme. The conclusion of this study provides recommendations on a minimum threshold of IMU sensor grades and general considersations that would be useful to the controller designer. The findings are demonstrated by observing the vertical acceleration, $n_{z}$ , angular rate setpoint tracking performance, and control surface deflections.
In response to the pressing challenges in parking online reservation platforms, the primary issue this paper addresses is the need for a user-centric parking reservation experience. To tackle this problem, the study a...
In response to the pressing challenges in parking online reservation platforms, the primary issue this paper addresses is the need for a user-centric parking reservation experience. To tackle this problem, the study aims to develop a recommendation system that enhances user satisfaction and streamlines the parking reservation *** provide personalized parking recommendations, a hybrid multimodal recommendation system is designed, grounded in distance-based recommendation and content-based filtering, and taking into account user preferences and feedback, history behavior and proximity to preferred tourist attractions and points of *** leveraging a rich dataset comprising 1804 parking items, results indicate a notable improvement and more user-centric user experience, as the system suggests parking lots in line with user preferences and points of interest. User feedback mechanisms are seamlessly integrated, facilitating continuous adaptation and refinement based on user convenience and past *** work shows significant potential in enhancing user satisfaction and streamlining the user experience in parking online reservation systems.
Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagn...
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ISBN:
(数字)9798331540821
ISBN:
(纸本)9798331540838
Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care.
Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medica...
Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated.
The development of cyber-attacks has unprecedented effect on businesses and governments. The recent years have witnessed various number of security breaches against organizations equipped by diverse cybersecurity solu...
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Service robots are undergoing a massification process similar to what happened with personal computers and cell phones a few decades ago. Their ubiquitous coexistence and interaction with humans requires that their re...
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ISBN:
(纸本)9798400706295
Service robots are undergoing a massification process similar to what happened with personal computers and cell phones a few decades ago. Their ubiquitous coexistence and interaction with humans requires that their representation models of the workspace go beyond metric information used for safe navigation. They are also required to assign semantic meaning to objects and places, i.e. to build semantic maps, in order to understand scenes and engage in human-like interactions. This paper proposes the Semantic MAPping (SMAP) framework to provide a service robot operating in human populated environments with a semantic mapping layer on top of a metric SLAM layer. SMAP is modular, expandable, and efficient enough to run locally on the robot. It has been implemented in Robot Operating System 2 (ROS2) using modular Docker containers. Preliminary experiments with a Pioneer 3-DX mobile robot having a system on module Nvidia Jetson AGX Xavier demonstrated its potential for future service robotics applications.
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