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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Transformative Insights: Harnessing machine learning for Big Data Analytics

Transformative Insights: Harnessing Machine Learning for Big...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Chandradeep Bhatt Divas Sagta Rahul Chauhan Shiv Ashish Dhondiyal Sanjay Sharma Dev Baloni Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Quantum University Dehradun India

Like oil in the past, data has become a very important resource in the modern world. But just as oil must be refined in order to be valued, data must also be handled and evaluated in order to yield worthwhile insights. Big Data analytics is a discipline that has developed in response to the expanding demand for efficient data analysis and interpretation. In order to find hidden patterns, connections, and insights, it involves examining huge, complicated datasets utilizing cutting-edge tools and methods. Big Data analytics' importance stems from its capacity to decipher the secrets contained in any dataset and glean insightful information. When appropriately analyzed and addressed, these insights can produce beneficial results. Big Data Analytics is extremely important in the context of consumer behavior. This technology is being used by marketers more frequently to analyze customer behaviors more accurately. Marketers can better understand their target market's preferences, demands, and buying habits by analyzing customer behavior. With the use of this information, marketers can make more informed choices and modify their marketing plans to specifically address customer needs. In the end, this may help to improve firm performance outcomes including boosted sales, enhanced client contentment, and general expansion. The aforementioned study seeks to offer a thorough overview of Big Data technology, including its goal, importance, and potential applications. It also looks into how Big Data technologies affect customer behavior and overall business effectiveness. The goal of this study is to shed light on these elements and the potential advantages and consequences of applying big data analytics across different fields.

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Privacy Protection in Federated learning Based on Differential Privacy and Mutual Information 2021

Privacy Protection in Federated Learning Based on Differenti...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Shibo Wang Yunbo Li Aowei Zhao Qiangmin Wang Shanghai Jiaotong University China

ISBN: (纸本)9781450385046

Federated learning is a distributed machine learning framework. On the premise of ensuring the legitimacy and compliance of data and the security of user privacy, it realizes the joint modeling of multi-party computer groups, in order to improve the accuracy of model fitting. Aiming at the privacy security of Federated learning user data, this paper uses Laplace distributed noise and weighted aggregation algorithm with mutual information as scoring mechanism to protect user data privacy. During the experiment, the research group realized the protection algorithm proposed in the paper based on the fat platform and obtained the optimal noise parameters through multiple groups of experiments, so as to minimize the impact of the protection mechanism on the model fitting accuracy and protect the privacy and security of the data to the greatest extent. In addition, the research group reproduces the previous attack algorithm and defense Algorithm for comparative experiments, so as to verify the security and accuracy of this algorithm.

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Enhancing Zero-Day Attack Detection with XAI-Driven ML Models and SMOTE Analysis

Enhancing Zero-Day Attack Detection with XAI-Driven ML Model...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： C.K. Sruthi Aswathy Ravikumar Harini Sriraman School of Computer Science & Engineering VIT Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Zero-day attacks, which are defined by their abrupt appearance without any previous detection mechanisms, present a substantial obstacle in the field of network security. To address this difficulty, a wide variety of machine learning and deep learning models have been used to identify and minimize zeroday assaults. The models have been assessed for both binary and multi-class classification situations, The objective of this work is to do a thorough comparison and analysis of these models, including the impact of class imbalance and utilizing SHAP (SHapley Additive exPlanations) explainability approaches. Class imbalance is a prevalent problem in cybersecurity datasets, characterized by a considerable disparity between the number of attack cases and non-attack instances. By equalizing the dataset, we guarantee equitable depiction of both categories, so preventing prejudice towards the dominant category throughout the training and assessment of the model. Moreover, the application of SHAP XAI facilitates a more profound comprehension of model predictions, empowering analysts to analyze the fundamental aspects that contribute to the detection of zero-day attacks.

关键词： Training Deep learning Analytical models Additives Explainable AI Predictive models Network security

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Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence 1

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Changkai, Lu Yiwen, Liu Yanxia, Gao Yuanquan, Shi Xiaoning, Peng School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Gesture recognition

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Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System

Obstacle Detection Using Faster R-CNN Oriented to an Autonom...

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international conference on Information and computer Technologies (ICICT)

作者： Javier Orlando Pinzón-Arenas Robinson Jiménez-Moreno Universidad Militar Nueva Granada Bogota Colombia

ISBN: (数字)9781728172835

ISBN: (纸本)9781728172842

Obstacle detection has been a relevant issue for the implementation of autonomous robotic systems, within which increasingly robust algorithms have begun to be applied, especially Deep learning techniques. However, these have not been widely used for the detection of obstacles in static robotic agents, contrary to what happens with mobile agents. For this reason, this work explores the use of one of these techniques, which is a neural network based on the Faster R-CNN, focused on detecting a specific obstacle (hands) in an application environment for a food assistance robot. For this purpose, a database containing 6205 training images and 1350 validation images was prepared, where 31 users perform different movements with their hands. To verify the capacity of the network, 3 architectures of different depths were implemented, which were evaluated and compared, resulting in the network of greater depth obtained the highest accuracy, of 77.4%, taking into account that the hands are not only still but also in movement, generating distortion in them and greater difficulty for their detection. Also, the internal behavior of the network was visualized through activations, to verify what it had learned, showing that it managed to focus on the hands, with some activations located in parts of the user's body such as face and arm.

关键词： Training Databases Task analysis Manipulators Robustness machine learning

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A Cutting Edge Deep learning Models for Paddy Leaf Disease Detection and Classification

A Cutting Edge Deep Learning Models for Paddy Leaf Disease D...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： A. Dhanalakshmi K. Ponmozhi A. Ajmal Department of Computer Applications Kalasalingam Academy of Research and Education KrishnanKovil Tamil Nadu India Department of Data Science Vellore Institute of Technology(ICER) Bangalore Karanataka India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The world’s main grain crop is rice (Oryza sativa). It provides the majority of the world’s population with nourishment and energy. Numerous rice leaf diseases are a major threat to the world’s agricultural output because they deteriorate crop quality in addition to lowering crop yield. Traditional methods of classifying and managing diseases mostly rely on laborious and subjective manual examination procedures. However, recent advancements in computer vision methods have made it possible to construct automated systems that can recognize illnesses from picture and video data. The paper emphasizes the potential application of cutting-edge models like Detectron2 to address issues with agricultural leaf disease detection. Together with other cutting-edge models, Detectron2 provides a promising means of enhancing the accuracy and effectiveness of disease diagnosis and control techniques. The use of automated disease detection systems holds the potential to reduce subjectivity in the evaluation of illnesses, optimize the allocation of resources in agriculture, and reduce dependence on human labour. These systems are able to continually learn from and adapt to changing disease patterns by incorporating complex algorithms and machine learning techniques, ensuring robust and stable agricultural operations.

关键词： Deep learning computer vision Adaptation models Computational modeling Scalability Sociology Agriculture

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and machine learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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Hybrid Deep learning and Ensemble Methods for Alzheimer’s Detection from EEG Signals

Hybrid Deep Learning and Ensemble Methods for Alzheimer’s D...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Mokara Anjali G. Naveen Sundar Yetukuri Gana Bala Meghana Beereddy Sreeja Narmadha Senthil Kumar Thangavel Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of AIML Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Alzheimer’s disease (AD) is a progressive neurological disorder characterised by aberrant behaviour, memory loss, and cognitive impairment. Electroencephalography (EEG) is an efficient method that provides valuable information on brain activity and can be used to predict AD. The application of EEG data for AD classification is examined in this abstract, with particular attention paid to the study of frequency bands, and machine learning (ML) techniques. This algorithms classifies AD with excellent levels of accuracy, indicating possibility of EEG-based methods for early disease identification. In order to classify AD in its early stages, this research proposes a unique deep learning model based on convolutional neural networks (CNNs) and signal processing techniques. The proposed model aims to classify patients into two categories: AD positive and negative. At the early stage the dataset was preprocessed using techniques such as label encoding and noise removal. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) are the techniques applied in signal processing. The classification is done using Deep learning (DL) and machine learning (ML) methods are used to develop the best model. ML algorithms namely Stacking classifier, Gradient Boosting, Stacknet, AdaBoost, and DL algorithms like Convolutional Neural Networks (CNN) and Deep Neural Network (DNN) architectures have shown promise in AD classification. Random Forest and AdaBoost excel in ensemble learning, while CNN and DNN models capture intricate patterns in EEG data, further enhancing the accuracy of diagnosis. In conclusion, EEG data offer non-invasive and economical method for classification of AD and contain useful information.

关键词： Deep learning machine learning algorithms Accuracy Signal processing algorithms Brain modeling Electroencephalography Classification algorithms Convolutional neural networks Ensemble learning Alzheimer's disease

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Identification of Tropical Dry Forest Transformation from Soundscapes Using Supervised learning 3rd

Identification of Tropical Dry Forest Transformation from ...

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3rd international conference on Smart Technologies, Systems and applications, SmartTech-IC 2022

作者： Castro-Ospina, Andrés E. Rodríguez-Buritica, Susana Rendon, Nestor Velandia-García, Maria C. Isaza, Claudia Martínez-Vargas, Juan D. Instituto Tecnológico Metropolitano Medellín Colombia Alexander Von Humboldt Institute Bogotá Colombia SISTEMIC Engineering Faculty Universidad de Antioquia-UdeA calle 67 No. 53 - 108 Medellín Colombia Universidad EAFIT Medellín Colombia

ISBN: (纸本)9783031322129

Biodiversity loss in tropical ecosystems is advancing due to factors such as human pressure and climate change, and determining their ecological transformation level can help to monitor climate change trends. Soundscapes can indicate the health of an ecosystem;therefore, passive acoustic monitoring is interesting because of its non-invasive nature, low cost, and allows recording over long periods. However, soundscapes generate a large amount of data that must be analyzed using automatic techniques such as machine learning to reduce manual labor. This process is generally based on acoustic indices, which present limitations such as sensitivity to background noise or biased results to the study sites under evaluation. Therefore, in this document, we propose training supervised models to identify landscape transformation from studied site soundscape at three stages: low, medium, and high. These models are trained on data collected in 24 tropical sites in the Colombian Caribbean region from 2015 to 2017. To achieve this, we extracted high-level features from a VGGish architecture from each recording to assess the performance of features obtained from the raw time series with neural networks. Furthermore, we compared the features from VGGish with 60 acoustic indices as features to train Random Forest, Neural Network, and XGBoost classifiers. F1-score for test data accomplishes a performance of 90% with the XGBoost classifier on the acoustic indices set, and the neural network achieves 95% for the VGGish feature set. Through supervised models, it is possible to identify the level of transformation of an ecosystem from its soundscape. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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Location-Free CSI-Based Lightweight Deeping learning Model for Human Activity Recognition

Location-Free CSI-Based Lightweight Deeping Learning Model f...

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Computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Xin Jiang Bin Li Yirui Du Daosen Zhai Ruonan Zhang School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

In recent years, activity recognition based on channel state information(Csi) has a wide range of application scenarios in the field of human-computer interaction(HCI) due to its advantages such as no need to carry special equipment, no privacy disclosure, and no light intensity. Many approaches based on traditional machine learning and deep learning have encountered two challenges. One is that the problem of activity recognition limited to fixed locations and complex backgrounds remains unsolved. The second is that some studies require complex CSI processing, which increases the network parameters, significantly lengthens the recognition time and raises the deployment cost. To this end, this study develops a Wi-Fi-based location-independent lightweight recognition model. We propose a lightweight CSI processing strategy that is able to effectively extract the main relevant features while compressing the model size. We combine 3D convolution with spatio-temporal information selection gates to extract activity-related information from CSI data, and employ knowledge distillation techniques to migrate the features learned from this model to a simple model. Extensive experimental results show that our system outperforms other deep learning models in terms of efficiency, with recognition accuracy up to 98.6%.

关键词： Deep learning Solid modeling Accuracy Three-dimensional displays Computational modeling Logic gates Feature extraction Real-time systems Data models Human activity recognition

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