For a variety of applications remote navigation of an unmanned aerial vehicle (UAV) along a flight trajectory is an essential task. For instance, during search and rescue missions in outdoor scenes, an important goal ...
For a variety of applications remote navigation of an unmanned aerial vehicle (UAV) along a flight trajectory is an essential task. For instance, during search and rescue missions in outdoor scenes, an important goal is to ensure safe navigation. Assessed by the remote operator, this could mean avoiding collisions with obstacles, but moreover avoiding hazardous flight areas. State of the art approaches enable navigation along trajectories, but do not allow for indirect manipulation during motion. In addition, they suggest to use egocentric views which could limit understanding of the remote scene. With this work we introduce a novel indirect manipulation method, based on gravitational law, to recover safe navigation in the presence of hazardous flight areas. The indirect character of our method supports manipulation at far distances where common direct manipulation methods typically fail. We combine it with an immersive exocentric view to improve understanding of the scene. We designed three flavors of our method and compared them during a user study in a simulated scene. While with this method we present a first step towards a more extensive navigation interface, as future work we plan experiments in dynamic real-world scenes.
作者:
R. Rajesh KannaT. Mohana PriyaV. SivakumarChandrasekharan NatarajJishamol ThomasAssistant Professor
Department of Computer Science CHRIST (Deemed to be University) Bangalore India Associate Professor
School of Computing Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Senior Lecturer
School of Engineering Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Research Scholar
Department of Sociology and Social Work CHRIST (Deemed to be University) Bangalore India
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass...
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem.
Phishing is a typical assault on unsuspecting individuals by making them to reveal their one-of-a-kind data utilizing fake sites. The target of phishing site URLs is to purloin the individual data like client name, pa...
Phishing is a typical assault on unsuspecting individuals by making them to reveal their one-of-a-kind data utilizing fake sites. The target of phishing site URLs is to purloin the individual data like client name, passwords and web based financial exchanges. Phishers utilize the sites which are outwardly and semantically like those genuine sites. As innovation keeps on developing, phishing strategies began to advance quickly and this should be forestalled by utilizing against phishing systems to recognize phishing. AI is a useful asset used to endeavor against phishing assaults. We as a whole know bunches of assaults are happening continuously situation in light of phishing URLS. There is no programmed procedure has been set up so far Multiple assaults of phishing URLs has not yet coordinated. In the proposed framework finding the phishing assaults/URLs, the System will identify various phishing assaults in equal succession and caution the ordinary clients with respect to phishing URLs.
作者:
Ashritha R MurthyK M Anil KumarAssistant Professor
Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Associate Professor
Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University
Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual's emotional state from textual data is an active area of stud...
Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual's emotional state from textual data is an active area of study, along with identifying emotions from facial and audio records. The study of emotions can benefit from many applications in various fields, including neuroscience, data mining, psychology, human-computer interaction, e-learning, information filtering systems and cognitive science. The rich source of text available in the Social media, blogs, customer review, news articles can be a useful resource to explore various insights in text mining, including emotions. The purpose of this study is to provide a survey of existing approaches, models, datasets, lexicons, metrics and their limitations in the detection of emotions from the text useful for researchers in carrying out emotion detection activities.
Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual syst...
详细信息
ISBN:
(数字)9781728127910
ISBN:
(纸本)9781728127927
Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.
The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that...
详细信息
Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) mode...
详细信息
Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models.
作者:
Andrew MosesG. Bharadwaja KumarStudent
School of Computer Science and Engineering Vellore Institute of Technology Chennai India Professor
School of Computer Science and Engineering Vellore Institute of Technology Chennai India
In today's technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and...
In today's technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and clarifications for all their doubts and questions. On many occasions, we are unable to find the exact word or meaning which we are searching for. So, if we have a clear, concise summary of a piece of literature, and we could understand what it contains with just a glimpse, we would be able to save a lot of time. This paper dwells about utilizing Natural Language Processing (NLP) to summarize a given text/textbook/paper. The state-of-the-art technology in this field has been demonstrated by Google's Bidirectional Encoder Representations from Transformers (BERT), one of the latest developments in NLP. BERT is believed to understand English better than other models because of its underlying bidirectional architecture. The present proposal is to use BERT as a sentence similarity extractor. By applying the TextRank algorithm, the sentences holding the most important information are extracted. This comes under the domain of extractive summarization. Abstractive summarization is much talked about, but since Google BERT is not built for generating text, we are utilizing it in a different way to achieve the requirement. This paper intends to discuss the use of BERT for the gen-next kids which will save time and initiate further interest for researchers in developing new programs continuously in the future.
computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considere...
详细信息
Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The com...
Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.
暂无评论