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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： S. Vimal S.Vishnu Shiva Prakash P.S. Surendar R. Veeravasanthan S. Sumathi Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of CSE(AI&ML) Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Because chronic illnesses are becoming more common and the aging population requires better drug administration, a Smart Pill Dispenser was created. This device aims to improve drug adherence by monitoring medication intake, giving reminders, and automating the pill distribution process. The Smart Pill Dispenser has an easy-to-use interface for effortless interaction, a microprocessor for system administration, and sensors to detect pill removal. The device also has remote monitoring features that let caregivers keep an eye on patients’ adherence in real time. The combination of these functions guarantees that medications are taken on schedule, lowering the possibility of missing doses and enhancing patient outcomes overall.

关键词： Drugs Schedules Automation Microprocessors Aging Real-time systems Sensor systems Safety Remote monitoring Intelligent sensors

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Geological Information Extraction from Satellite Imagery Using Deep Learning

Geological Information Extraction from Satellite Imagery Usi...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. PushpaRani G. Roja R. Anusha C. Dastagiraiah B. Srilatha B. Manjusha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE(AI & ML) & CSE(DS) GNITS Shaik pet Hyderabad Department of Computer Science and Engineering Anurag University Hyderabad India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling.

关键词： Deep learning Geology Architecture Buildings Vegetation mapping computer architecture Feature extraction Information retrieval Satellite images Data mining

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An Enhanced Diagnostic System using Deep Learning for Early Prediction of Pancreatic Cancer

An Enhanced Diagnostic System using Deep Learning for Early ...

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International Conference on Electrical Energy Systems (ICEES)

作者： Kumbham Bhargavi M Lakshmi Prasad Arun G Pundru Chandra Shaker Reddy S Yuvalatha M Naga Triveni Department of CSE (AI & ML) Keshav Memorial Institute of Technology Hyderabad Telangana India Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and Business Systems Anna University Bannari Amman Institute of Technology Tamil Nadu India Department of CSE (AI&ML) St. Martin's Engineering College Hyderabad Telangana India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

With a dismal 5-year survival-rate of approximately 7%, pancreatic-cancer(PC) remains one of the world's most lethal tumors. In order to improve patient survival rates, early detection of PC is essential. computerized-tomography(CT), MRI combined with MRCP, or a biopsy is necessary for the diagnosis of PC. The steps involved in the suggested CAD design process are as follows: picture preprocessing, segmentation, feature-extraction & classificatiion. Color conversion and the isotropic diffusion filter method are utilized for preprocessing. The next step is the segmentation processes' usage of the suggested Fuzzy K-NN Equality code. A classification tool that makes use of Deep Learning is feature extraction. Using the characteristics gathered from the pancreatic sample, tumor cells are categorized. The image classification criterion includes both the train values and the testing datasets. To identify pancreatic cancer, an algorithm called DCNN_DBN is employed, which combines Deep-Convolutional Neural-Networks with Deep-Belief-Networks. The results of the experiments show that the present CAD system has great promise and is safe for the automated diagnosis of benign & malignant tumors, with an accuracy rate of 99.8 percent. The use of this classifier greatly reduces the computational complexity. More anomalies in pancreatic cancer cells could be detected with an improved version of the proposed method.

关键词： Deep learning Image segmentation Accuracy Sensitivity Image color analysis Databases Roads Pancreatic cancer Classification algorithms Testing

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Advanced Sports Performance Analysis using Deep Learning for Posture and Movement Identification

Advanced Sports Performance Analysis using Deep Learning for...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Arvind, S. Hemalatha, K.L. Upendra Roy, B.P. Sandeep, Kasturi Sai Pareek, Piyush Kumar Hyderabad Institute of Technology and Management Department of Cse Hyderabad India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

The proposed work explains the method of detecting the area of interest (ROI), extracting pertinent information, distinguishing different postures, and categorising objects, activities, or events by employing recovered characteristics and segmented regions. Furthermore, the paper delves into the challenges of analysing human posture in three dimensions and advises using 3DPoseNet to estimate the 3D coordinates of vertices. Furthermore, the essay investigates the occlusion problem and proposes a general solution. This style involves extensive use of the torso as well as the limbs and head. Finally, the paper emphasises the creation and implementation of deep learning (DL) in motion identification, which has increased detection performance while decreasing complexity. The article emphasises the importance of developing a variety of algorithms and strategies, such as object tracking, event detection, and action identification, as well as combining methods from image processing, machine learning, and computer vision in order to analyse and interpret video data in applications such as surveillance, sports analysis, and medical imaging. Surveillance, sports analysis, and medical imaging are examples of these uses. © 2023 IEEE.

关键词： Extraction

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Non-invasive estimation of the powder size distribution from a single speckle image

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Light(science & Applications) 2024年第10期13卷 2221-2230页

作者： Qihang Zhang Ajinkya Pandit Zhiguang Liu Zhen Guo Shashank Muddu Yi Wei Deborah Pereg Neda Nazemifard Charles Papageorgiou Yihui Yang Wenlong Tang Richard D.Braatz Allan S.Myerson George Barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 117543Singapore Department of Chemical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Process Chemistry Development Takeda Pharmaceuticals International Co40 Landsdowne StCambridgeMA 02139USA ShinrAI Center for AI/ML Data Sciences InstitutesTakeda Pharmaceuticals International Co650 E Kendall StCambridgeMA 02142USA Present address:Department of Precision Instruments Tsinghua UniversityBeijing 100084China

Non-invasive characterization of powders may take one of two approaches:imaging and counting individual particles;or relying on scattered light to estimate the particle size distribution(PSD)of the *** former approach runs into practical difficulties,as the system must conform to the working distance and other restrictions of the imaging *** latter approach requires an inverse map from the speckle autocorrelation to the particle *** principle relies on the pupil function determining the basic sidelobe shape,whereas the particle size spread modulates the sidelobe *** recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network,trained efficiently through a physics-informed semi-generative *** this work,we eliminate one of the most time-consuming steps of our previous method by engineering the pupil *** judiciously blocking portions of the pupil,we sacrifice some photons but in return we achieve much enhanced sidelobes and,hence,higher sensitivity to the change of the size *** result is a 60×reduction in total acquisition and processing time,or 0.25 seconds per frame in our *** real-time operation in our system is not only more appealing toward rapid industrial adoption,it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying,blending,and other chemical and pharmaceutical manufacturing processes.

关键词： speckle size distribution

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Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Mudit Mittal Archana Sehgal Neeraj Varshney Sunil Prashanth Kumar Nandini Shirish Boob R. Akhilesh Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara Department of Computer Engineering and Applications GLA University Mathura Department of Mechanical Engineering New Horizon College of Engineering Bangalore Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things.

关键词： Adaptation models Smart cities Computational modeling Scalability Transportation Traffic control Deep reinforcement learning Real-time systems Planning Internet of Things

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Real-Time Gesture Recognition for Volume Control Using Python and ml Techniques 5

Real-Time Gesture Recognition for Volume Control Using Pytho...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, Syed Murugan, Lavanya Mutharasu, M. Vadivel, T. Fathima, H. Leelavathi, M. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639 113 India American International University Computer Science Kuwait Andhra Pradesh Madanapalle517325 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India Sree Saraswathi Thyagaraja College Department of AI & ML Tamil Nadu Pollachi642205 India

ISBN: (纸本)9798331530013

This research study proposes a gesture-based volume control system that combines computer vision and machine learning to improve user interaction with audio devices. By applying the OpenCV library for real-time video processing and MediaPipe's hand-tracking module, the system interprets hand gestures to control the volume. This system measures the distance between the index finger and thumb. Using this distance, the system adjusts the system's audio volume by using the Pycaw library. The purpose is to have a touchless interface wherein the user can control audio levels by simple hand movements to enhance accessibility and usability across various environments. This is an promising alternative to traditional input methods as the demand for more natural human-computer interaction grows, this gesture-based approach represents a leveraging computer vision and machine learning technique, which accurately detects gestures in volume control. The goal of this project is to design a touchless volume control system that relies on gesture input, computer vision, and machine learning techniques in order to bring an intuitive user experience. The proposed system integrates OpenCV for real-time video processing and makes use of Media Pipe's advanced hand-tracking abilities to identify gestures, which are the distance between the thumb and index finger, to control the volume of audio. Pycaw is used to interact with the audio output of the system to dynamically control the volume. This gesture-based interface is designed to improve accessibility, usability, and user experience by providing a modern, intuitive alternative to traditional volume control methods. © 2024 IEEE.

关键词： Gesture recognition

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Self-moving Window with Long-Short Term Memory based Anomaly Detection in Edge-Assisted IoT Environments

Self-moving Window with Long-Short Term Memory based Anomaly...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yerrolla Chanti Myasar Mundher Adnan R. Renuga Devi Lakshmanan M Arelli Madhavi School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram India Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the detection of anomalies accurately which lead to risk of losing information. To overcome this limitation, a Self-moving Window with Long Short-Term Memory (SW-LSTM) is proposed for accurate detection of anomalies. The self-moving window improved the ability of LSTM to identify subtle anomalies that evolve over time, leading to more accurate detection of unusual patterns or device behavior in IoT environments. Initially, the data about attacks and malicious nodes are acquired from the UNSW-15 dataset and preprocessed by score normalization technique. Then, the optimal set of features are selected by Particle Swarm Optimization (PSO) algorithm. Finally, the proposed SW-LSTM model detects the anomalies in edge assisted IoT network effectively. The experimental results of proposed method achieved accuracy of 93.4% which is greater when compared to existing detection approaches such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN).

关键词： Accuracy Recurrent neural networks Image edge detection Computational modeling Internet of Things Convolutional neural networks Object recognition Particle swarm optimization Long short term memory Anomaly detection

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An Efficient Wafer Semiconductor Surface Defect Inspection Using Radial Basis Functional Neural Network 2

An Efficient Wafer Semiconductor Surface Defect Inspection U...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Dineshkumar, R. Kumar, Komuravelly Sudheer Murugan, Chitra Al-Fatlawy, Ramy Riad Harshitha, P. Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha School Of Engineering Department Of Ece Chennai India Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Rajalakshmi Institute Of Technology Department Of Electronics And Communication Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Menashaki Institute Of Technology Department Of Ai Ml Bengaluru India

ISBN: (纸本)9798350381665

Semiconductor wafer discoveries widespread application in unmanned material delivery vehicles, repairing robots, spray androids, and recognition station sensors. the semiconductor cracker's flaws, such as holes, husks, cuts, and stains that are created during the production process have a negative impact on the quality of products that are produced later. As a result, it was authoritative to inspect wafer defects. Conventional techniques for inspecting semiconductor wafer defects that rely on handcrafted features are limited in some application scenarios and strongly dependent on experience. This research proposes a unique approach for semiconductor wafer surface defect inspection using deep convolutional neural networks. Initially, in order to excerpt features and create feature maps, a innovative structure for feature pyramid networks with atrous convolution (FPNAC) is designed. Secondly, region proposals are generated by feeding the feature plots into the region proposal network (RPN). In order to correctly categorize and segment the flaws, the region recommendations are finally associated to matching size by way of the inputs of a Radial Basis Functional Neural Network (RBFNN), which consists of three branches. The suggested RBFNN produces good overall performance, as evidenced by the experimental findings, which show Mean Intersection over Union (MIoU) of 90.06% and Mean Pixel Accuracy (MPA) of 94.97%. © 2024 IEEE.

关键词： Convolution

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ml Models and Big Data Analytics to Predict the Cricket Match

ML Models and Big Data Analytics to Predict the Cricket Matc...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： V. Asha S. Sreekanth Boddu Rajasekhar Ginni Nijhawan Tamam Ali Abd Ulabbas Abedi Ashish Parmar Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India LPU Phagwara India Medical Technology The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

artificial intelligence (ai)and Machine learning have greatly improved the accuracy of cricket, one of the most popular, well-supported, and exhilarating sports of today. The quantity of matches played throughout time has led to an exponential growth in the data about cricket matches and individual players. Furthermore, big data analytics are in high demand, and this data has many potential applications. Some examples of its potential applications include team player selection and prediction utilizing machine learning models and significant data approaches. A machine learning linear reversion model was used to forecast the team's performance in the absence of massive data and the comprehensive Spark ml framework. Following the application of linear regression in Spark ml, the untried outcomes are evaluated using accuracy, mean square error (MSE) and mean absolute error (MAE) root mean square error (RMSE), which are reported as 95%, 30.2, 1350.34, and 28.2 correspondingly. More than that, may use this method for other kinds of sports.

关键词： Analytical models Accuracy Linear regression Urban areas Machine learning Predictive models Big Data Data models Sparks Sports

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