作者：
Shobanadevi, A.Kottu, SreekanthKumar, K. R. SenthilAmudha, K.Praveena, K.Venkatesh, R.School of Computing
Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University
Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College
Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India
Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University
Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India
Department of Physics Tamil Nadu Dindigul624622 India
This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- qu...
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Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine ope...
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The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS). Recursive techniques for reachability analysis ...
The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS). Recursive techniques for reachability analysis of dynamical systems in closed-loop with a NN controller, planner or perception can over-approximate the reachable sets of the NNDS by bounding the outputs of the NN and propagating these NN output bounds forward. However, this recursive reachability analysis may suffer from compounding errors, rapidly becoming overly conservative over a longer horizon. In this work, we prove that an alternative one-shot reachability analysis framework which directly verifies the unrolled NNDS can significantly mitigate the compounding errors, enabling the use of the rolling horizon as a design parameter for verification purposes. We characterize the performance gap between the recursive and one-shot frameworks for NNDS with general computational graphs. The applicability of one-shot analysis is demonstrated through numerical examples on a cart-pole system.
作者：
Sikka, DhruvRajeswari, D.School of Computing
College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing
Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India
Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the...
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Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses w...
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ISBN:
(数字)9798350352689
ISBN:
(纸本)9798350352696
Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.
The quick rise in diabetes cases globally underscores the essential need for technology-driven, innovative management solutions. The project gives the introduction about the cutting-edge in AI-based chatbot tailored f...
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ISBN:
(数字)9798331513863
ISBN:
(纸本)9798331513870
The quick rise in diabetes cases globally underscores the essential need for technology-driven, innovative management solutions. The project gives the introduction about the cutting-edge in AI-based chatbot tailored for diabetic patients. The combination of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), results the chatbot to provide highly accurate, personalized recommendations to improve self-management. The added advantage is the inclusion of voice-based interaction which offers enhanced accessibility, especially for the elders and visually impaired persons. This study demonstrates about the ability of the chatbot to interpret the complex health data, delivers the real-time insights, and promote adherence to the treatment plans. The advancement in the fusion of machine learning algorithms and voice interaction secures a dynamic and user-friendly experience for diabetes management.
We present an amplitude-invariant phase shifter based on a PIN diode embedded in a Mach-Zehnder Interferometer for optical phased arrays. The dynamic power contrast is lower than 0.42 dB over a π phase shift. CLEO 20...
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Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the othe...
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Based on a resistance-capacitance equalizer, we demonstrated a silicon thermo-optic phase shifter with a rise time of 2.3 µs and a modulation bandwidth of 250 kHz, offering 10 times enhancement over the original ...
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Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze ...
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Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL
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