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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Neeraj Singla Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Anything non-living that can take decisions on its own is called artificially intelligent. It is a technique designed to mimic human behaviour. On the other hand machine learning if a subset of artificial intelligence. It is a statistic method used to enable machine to improve with experience. They both go hand in hand. Now IoT has been there in existence for an enormous amount of time but during recent times due to large scale good connectivity for efficient data transfer, machine learning and analytics and Conversional Artificial intelligence has made IoT possible.

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Sensitive Data Classification of Imbalanced Short Text Based on Probability Distribution BERT in Electric power industry

Sensitive Data Classification of Imbalanced Short Text Based...

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2023 international conference on pattern recognition, machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Zhang, Wensi Liang, Xiao Zhang, Yifang Su, Hanchen State Grid Smart Grid Research Institute Co.Ltd. Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Beijing China

ISBN: (纸本)9798350346596

The exploitation of big data in industrial fields faces several challenges, such as data privacy and security, data integration and interoperability, and data analysis and visualization. Data privacy and security is a major concern, as the data collected from industrial fields often contain sensitive information. Due to the particularity of the industrial field, there are challenges in the utilization of big data. 1. The distribution of different categories data is extremely uneven;2. There are a large number of industry terms in the short texts that constitute the metadata, which makes semantic representation difficult. These two challenges have a large impact on the application performance of existing models. In order to resolve the problems above, this paper proposes a pre-training model based on probability distribution, which for the classification of sensitive data in the power industry. The model consists of three modules: 1. The data enhancement module adopts the technology of synonym expansion and noise introduction, so that the model can extract the classification features of sensitive data with a small proportion;2. The pre-training module adopts the BERT model, which can obtain the semantics of industry terms in short texts;3. The probability prediction module is used to regularize the distribution of test data to meet the training data. Compared with the traditional classification model and the classification model based on deep learning, the F1-score can be improved by 36.68% and 6.39%. © 2023 IEEE.

关键词： Probability distributions

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Framework for the Deployment of Intelligent Smart Cities (ISC) using Artificial intelligence and Software Networking Technologies 3

Framework for the Deployment of Intelligent Smart Cities (IS...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mohamed, Nachaat Upadhyay, Ramakant Jakka, Geethamanikanta Rambabu, P.V. Alfurhood, Badria Sulaiman Singh, Devesh Pratap Universiti Sains Malaysia Rabdan Academy UAE Homeland Security Abu Dhabi United Arab Emirates Sanskriti University Department of CSE Mathura Uttar Pradesh India University of the Cumberlands Data Analytics Information Technology United States Civil Engineering Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia

ISBN: (纸本)9798350399264

This article's goals include providing a framework for interpersonal and communication to safeguard welfare services and assess how well the innovation ecosystem is now understood. An essay starts with an overview or description of "urban area"This viewpoint includes neighborhood services, modern amenities, or smart infrastructure. Information and technology, digital operations use, monitoring, and interconnection, amongst other things. The section that follows examines various urban planning concerns, either administration or expansion. Utilizing diverse technologies effectively is made possible by innovative information about towns or an answer to a question concerning high-quality public infrastructure. An innovative method of managing networks is the Software Defined Network (SDN) architecture. Unlike switches in a conventional network computing environment, changes in SDN do not process inbound sachets in the similar means. If no inbound packets are identified after searching the linking tables, they are passed to the control system, the SDN's operating system, for processing. A Spread Denial of Service (DoS) outbreak poses the biggest risk to the cyber security of an SDN *** system or application layers of the disease-ridden devices connected to the system will be the target of the attack. The approach for determining whether incoming packets are contaminated or not is clever machine learning-based, and it is proposed in this paper. In the context of smart cities, the deployment of artificial intelligence (AI) has newly drawn more and more attention. The purpose of this study is to perform a works assessment to examine the role of AI in the various parts of smart towns given the relevance of the topic. This study's technique combines a thorough literature evaluation to find the most pertinent papers with reference system investigation to reveal the subtleties of the subject. The findings of this study highlight the significance of this technology, whic

关键词： Smart city

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AI-Driven Prediction of Medicinal Property Presence in Plants Cultivated with Nutrient-Enriched Manure 3rd

AI-Driven Prediction of Medicinal Property Presence in Plant...

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3rd international conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Khanna, Abhirup Jain, Sapna Choudhury, Tanupriya Kotecha, Ketan Sar, Ayan Uttarakhand Dehradun248007 India University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International University Pune India

ISBN: (纸本)9789819739905

For thousands of years, medicinal plants have been used for treating a variety of ailments. Medicinal plants have the potential to provide natural and effective remedies for common health problems and are often cost-effective and less toxic as compared to synthetic drugs. Despite the multiple advantages of medicinal plants, their cultivation is still a result of inefficient rudimentary practices. The lack of standardisation in cultivation practices, harvesting and processing of medicinal plants has led to consistency in the final product. Moreover, many farmers need more knowledge and technical expertise to grow medicinal plants, resulting in unsuccessful cultivation operations. The quality of soil and the right amount of macronutrients an important factors determining the growth and quality of medicinal plants. Extensive use of chemical fertilisers and inconsistent levels of micronutrients may lead to loss or diminishing medicinal properties. The works present a hypothesis that emphasises the use of machine learning algorithms for the prediction of medicinal properties. The study presents ways in which machine learning and AI can be used to analyse data from various sources to identify patterns and make predictions regarding the cultivation of medicinal plants. The hypothesis proposed in this research framework was tested with prior tests and also validated against three different concept cases, which ultimately catered to the cultivation and production of different medicinal plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medicinal chemistry

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RSVT 2022 - 2022 3rd international conference on Robotics Systems and Vehicle Technology

RSVT 2022 - 2022 3rd International Conference on Robotics Sy...

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3rd international conference on Robotics Systems and Vehicle Technology, RSVT 2022

ISBN: (纸本)9781450396783

The proceedings contain 13 papers. The topics discussed include: obstacle-recognition method for soccer robot based on human selective attention;development of an underwater cleaning robot with vortex suction mechanism;development of an underwater cleaning robot with vortex suction mechanism;analyzing the influence of driver, route and vehicle-related factors in electric vehicle energy consumption, based on real life data;abuse torque load simulation and design of differential mechanism in hybrid electric vehicle;estimation of maximum friction coefficient using recurrent artificial neural networks;design of intelligent protection device based on numerical control tool;research on working condition transformation of Huangshan test bench based on Markov braking vibration noise;machinery fault diagnosis using continuous wavelet transform and artificial intelligence based classification;and numerical study of the axial mechanical properties of braided corrugated hose with different braiding angles.

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Action recognition System Based 5G Communication and Edge Computing for Service Robot

Action Recognition System Based 5G Communication and Edge Co...

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Computer Communication and Artificial intelligence (CCAI), international conference on

作者： Yanming Zhang Cailun Wei Gangcan Sun Dawei Zhang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou Zhengzhou

Action recognition is one of the most critical technologies for the robot to provide service excellently. In order to solve the problems of low accuracy, limited arithmetic power, and low intelligence of service robot's action recognition, an action recognition system based on 5G communication and edge computing is proposed. Communication and data interaction between robot and edge computing device is realized by using 5G routers and technologies such as NAT and Socket. A LightSlowfast network is designed for the service robot, which improves action recognition accuracy while reducing the computational effort. The experimental results show that the Light-Slowfast network achieves 72.9% accuracy on the HMDB51 dataset. The system performs well in practical tests, and the low latency of data transmission can satisfy the robot's demand for real-time action recognition.

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Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial intelligence

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international JOURNAL OF SEMANTIC COMPUTING 2022年第1期16卷 29-45页

作者： Dye, Mike Stamps, D. Sarah Mason, Myles Saria, Elifuraha Ronin Inst 127 Haddon Pl Montclair NJ 07043 USA POB 56 Nederland CO 80466 USA Virginia Tech Dept Geosci 926 Campus Dr Blacksburg VA 24061 USA Virginia Tech Acad Integrated Sci 800 Campus Dr Blacksburg VA 24061 USA Ardhi Univ Dept Geospatial Sci & Technol POB 35176Observation Hill Plot 3Block LUni Salaam Tanzania

Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHOrdS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control ("noise reduction"), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHOrdS portal streams real-time GNSS positioning data in 1s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO's real-time GNSS data services. UNAVCO's real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points ("filter") that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous

关键词： TZVOLCANO CHOrdS UNAVCO artificial intelligence machine learning noise reduction unsupervised learning volcanic hazards assessment

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machine Learning for Mobile Defense: Detecting SMS Malware and Riskware on Android 3

Machine Learning for Mobile Defense: Detecting SMS Malware a...

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3rd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Singh, Anmol Rattan Singh, Gurjinder Jagadish, Sripelli Shnaeen, Amaar Haamid Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal 506371 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq

ISBN: (纸本)9798331521691

Android smartphones are frequently targeted by SMS Malware and Riskware attacks that aim to steal private information such as user credentials, photos, videos, and banking details. Often, Android users unknowingly allow these attacks by clicking on unknown links or messages. The goal of this research paper is to boost user confidence and privacy on Android devices. The CICMalDroid 2020 dataset, which includes data on traffic patterns related to Riskware and SMS malware attacks, forms the basis of this research. The study introduces a methodology for the early detection of these threats by incorporating a machine learning-enabled chip at the hardware level. The dataset has been processed using various machine learning algorithms to assess the most effective ones for detecting Android malware. The findings demonstrate the superiority of the random forest algorithm on the CICMalDroid dataset, with this classifier achieving the highest accuracy, recall, and F1 score compared to others. Conversely, the Naive Bayes method is noted for its reliability and its ability to minimize false positives. These outcomes emphasize the importance of strengthening cybersecurity measures, regularly updating software, and the critical role of machine learning in enhancing the security of Android devices. © 2025 IEEE.

关键词： Attack Defense machine Learning Malware Mobile Riskware SMS

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Third international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

Third International Conference on Advanced Algorithms and Si...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

ISBN: (纸本)9781510668522

The proceedings contain 210 papers. The topics discussed include: research progress and prospects of intelligent planning algorithms in navigation collision avoidance;research on determining the end point of carbon kneading based on LSTM algorithm;pavement grade recognition algorithm based on sprung mass acceleration;research on distributed network information security diagnosis method based on matrix algorithm;two-dimensional array antenna directional pattern optimization method based on genetic simulated annealing algorithm;air defense target allocation based on improved genetic algorithm;research on visible and infrared binocular image fusion algorithm based on improved NSCT;study and application of migration technology in ground penetrating radar (GPR) data processing;load forecasting analysis based on the residential electricity consumption data;performance of BOK-MPSK chirp modulation for data transmission;and anomaly detection method of power purchase material data based on B IRCH clustering algorithm and time series.

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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