As of late, there has been expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Ump...
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In landslide-prone regions, the timely detection and precise prediction of impending disasters are paramount for minimizing their devastating impact. by integrating edge devices endowed with AI capabilities into the e...
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ISBN:
(数字)9798350377002
ISBN:
(纸本)9798350377019
In landslide-prone regions, the timely detection and precise prediction of impending disasters are paramount for minimizing their devastating impact. by integrating edge devices endowed with AI capabilities into the existing iot infrastructure, this approach enables real-time data processing and analysis at the source, facilitating swift detection of early warning signs and prompt dissemination of alerts. Augmenting this setup with cloud resources enhances scalability and computational prowess, crucial for conducting comprehensive data analysis and predictive modeling. Anticipated outcomes include the development of a sophisticated monitoring system capable of accurately forecasting landslides, thereby enhancing preparedness and resilience in landslide-prone areas. The project envisions a scalable and adaptable framework that integrates seamlessly into existing infrastructure, offering a cost-effective and reliable solution for landslide surveillance and prediction.
Compressed sensing recovers the sparse signal from far fewer samples than required by the well-known Nyquist–Shannon sampling theorem to speed up the measurement procedure. The sparse signal recovery performance can ...
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This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, Th...
Every single day, millions of people all around the world utilize various social networking platforms. The usage of social media platforms like Twitter and Face book may result in both positive and bad results for use...
Every single day, millions of people all around the world utilize various social networking platforms. The usage of social media platforms like Twitter and Face book may result in both positive and bad results for users. These outcomes are not mutually exclusive. The most popular social networking sites have recently been a primary target for spammers who wish to disseminate large numbers of undesired and maybe harmful information. For example, Twitter has quickly become one of the most commonly used platforms ever, which has led to a large increase in the amount of spam that is posted on the network. Users who really use Twitter are irritated and frustrated by tweets from bogus accounts that promote meaningless products, services, or websites. There has also been an increase in the ease with which hazardous chemicals may be transmitted by presenting users with misleading information while using bogus identities. This has led to an increase in the likelihood that people would be harmed. In the field of contemporary social media, two of the most popular research subjects are the elimination of spam and the verification of users on Twitter. In the event that they continue to disseminate dangerous advertisements, spam accounts on social networking websites pose a substantial threat to the safety of the internet and should be eliminated as quickly as possible. This article explores the origins of spam accounts on social networks like Twitter, as well as the distinguishing characteristics of spam accounts, with the goal of improving spam identification. Twitter is one of the most popular online social networks (OSNs), and its members include ministers, business moguls, Hollywood actors, and Fortune 500 companies. The platform's 313 million monthly active users are responsible for publishing around 500 million tweets each and every month on it. Due to Twitter's rising popularity, spammers have developed an interest in the platform. These malicious actors exploit the servi
The successful development of a diagnosis system to identify diabetes in the Internet of Things (iot) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. iot is playing an...
The successful development of a diagnosis system to identify diabetes in the Internet of Things (iot) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. iot is playing an increasingly important role in healthcare environment by providing a structure for evaluating medical information to detect diseases via data mining techniques. The existing diagnostic methods have some challenges, such as lengthy calculation times and inaccurate predictions. To evade the limitations, this article suggested an iot-based diagnosis system that uses Machine Learning (ML) techniques. Through the dataset from UCI Repository with medical sensors, a novel systematic technique is utilized to treat diabetic disease, and relevant medical data is produced to precisely predict those who would be seriously impacted by the condition. For predicting the illness and its severity, a brand-new classification technique called Tuna Swarm optimization-Aided Neural Classifier (TSO-NN) is suggested (T. The experimentation is conducted in MATLAb and the performance is evaluated using accuracy, precision, and F1-score. besides, the efficiency is tested and confirmed by comparing over SOTA methods.
The need for increased sustainability and efficiency in the energy industry has led to a major increase in interest in the development of cyber-physical systems (CPS) in the smart grid domain in recent years. In the c...
The need for increased sustainability and efficiency in the energy industry has led to a major increase in interest in the development of cyber-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS" refers to a network of physical objects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distribution, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can be successfully implemented by utilizing CPS's capabilities. On the other side, blockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distributed, there is no need for middlemen, transparency is improved, and data integrity and immutability are all guaranteed. These qualities make blockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating blockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possible by blockchain, which enables safe and effective peer-to-peer energy trade among prosumers. This increases sustainability by encouraging the use of renewable energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainability through the smart grid's integration of blockchain technology with cyber-physical systems.
Comparing the effectiveness of Skin Condition Detection using ResNet, EfficientNet and MobileNetV2 deep learning models. The 3 skin conditions that have been considered are Acrochordon, Lichenoid Keratosis and Vascula...
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ISBN:
(数字)9798331530013
ISBN:
(纸本)9798331530020
Comparing the effectiveness of Skin Condition Detection using ResNet, EfficientNet and MobileNetV2 deep learning models. The 3 skin conditions that have been considered are Acrochordon, Lichenoid Keratosis and Vascular Lesions. The ResNet Model, EfficientNet Model and MobileNetV2 Model have been assessed against one another. ResNet and EfficientNet both predicted the skin condition with an accuracy of 92.1875% which is greater than MobileNetV2, which predicted the skin condition with 90.625% accuracy. Through this research, it has been concluded that ResNet and EfficientNet are significantly better than MobileNetV2 in terms of training models to accurately detect skin conditions.
Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, wh...
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ISBN:
(数字)9798331505134
ISBN:
(纸本)9798331505141
Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image pre-processing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection.
The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, po...
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ISBN:
(数字)9798350361155
ISBN:
(纸本)9798350361162
The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.
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