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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Jiang, Junhao Zuo, Yi Li, Tieshan Ma, He Navigation College Dalian Maritime University Dalian China Collaborative Innovation Center of Maritime Big Data and Shipping Artificial General Intelligence Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665443227

With the development of inland river transportation, the navigable conditions of inland waterways are becoming more and more complicated. Therefore, navigation with the aid of navigator alone is not enough. It is also necessary to make reasonable decisions on the navigation of the ship based on the future navigation behavior of the ship. Traditional trajectory prediction methods are difficult to accurately train models with the small amount of Automatic Identification System (AIS) data. In order to solve the problem of the data and the original models, this paper uses the Hermite interpolation method to expand the data. On this basis, we propose a model ship trajectory based on Gated Recurrent Unit (GRU) neural network. In the experiment, we select the AIS data of ships on the Huai'an section of the Beijing-Hangzhou Canal. Finally, Back Propagation Neural Network (BPNN) model and Support Vector Regression (SVR) model are conducted to a comparative experiments. The results show that the GRU neural network trajectory prediction model has high prediction accuracy, feasibility and effectiveness. © 2021 IEEE.

关键词： Ships

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A Smart IoT-Enabled Wildfire Monitoring and Early Detection System using Convolutional Neural Networks and Sensor data Analysis

A Smart IoT-Enabled Wildfire Monitoring and Early Detection ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： U.V. Anbazhagu A. Alice Blessie R. Santhana Krishnan G. Yamini J. Relin Francis Raj P. Sundaravadivel Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration with intelligent decision-making frameworks, leading to inefficient fire prevention strategies. The absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. This work presents an IoT-based deep learning approach utilizing Convolutional Neural Networks (CNNs) for real-time forest fire detection and prevention. data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. The system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. The key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. The integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. The proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.

关键词： Deep learning Wildfires Accuracy Prevention and mitigation Surveillance Decision making Manuals Real-time systems Satellite images Time factors Climate change Fires Detection algorithms

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Assessing the Efficiency of AI for Multivariate data Analysis

Assessing the Efficiency of AI for Multivariate Data Analysi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Jambi Ratna Raja Kumar Mukesh Chaturvedi D. ANandhasilambarasan V. Ashok Ashwini R. Nawadkar Manoj Va Department of Computer Engineering Genba Sopanrao Moze College of Engineering Pune Maharashtra India Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India

This study aimed to evaluate the efficiency of synthetic intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics—accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision.

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Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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Innovation in technology (INOCON), IEEE International Conference for

作者： Presitha Aarthi M Chilukala Mahender Reddy A. Anbarasi N. Mohankumar Ishwarya M.V S. Murugan Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Computer Science and Engineering GITAM University Hyderabad Telangana India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Hyderabad Telangana India Artificial Intelligence and Data Science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents.

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A Robust and Novel Hybrid Deep Learning based Lung Nodule Identification on CT Scan Images

A Robust and Novel Hybrid Deep Learning based Lung Nodule Id...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： A. Sivakumar Lilly Sheeba S M. Suganthy B. Gunasundari S. Sivamurugan Sajiv G Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Computer Science Engineering SRM Institute of Science and Technology Ramapuram Chennai Department of Electronics and Communication Engineering VeltechMultitechDr.RangarajanDr.Sakunthala Engineering College Chennai Department of Computer Science Engineering Prathyusha Engineering College Chennai Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai

Due to its aggressiveness and late identification in advanced stages, lung cancer is one of the leading causes of cancer-related mortality. Lung cancer is fatal if not treated early, thus this is a major obstacle to overcome. If the nodule is not malignant, it will not metastasize. Oftentimes, malignant nodules will quickly disperse. Lung cancer is one of several potentially lethal forms of the disease. Millions of lives are lost every year because of it. In order to analyses the nodule in its precancerous stages, a very effective technique is required. Nodule detection in CT scan data is challenging; this must be improved for subsequent therapy to be successful. For many years, CT scans have been used to identify nodules for potential treatment. The radiologist's existence and size assessment of the nodule is subject to error. The procedure is not without flaws. Radiologists will examine the CT scan results side-by-side to determine the nodule's location and health. A reliable method is required to identify the tumor in CT scan pictures and supply physicians with an understandable computerized assessment report. The purpose of this research was to create and test a Novel Hybrid Neural Network (NHNN) model for nodule detection using image-based features. This provides the radiologist with more information that can be used to detect cancer in its earliest stages, when it is still contained within the nodule. During the evaluation of performance, many factors were considered along with precision. With an efficiency of 96.8%, precision of 97.47%, and specificity of 96.64%, the proposed model is highly effective

关键词： Deep learning Computed tomography Neural networks Lung cancer Medical treatment Lung Knowledge discovery

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LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments

LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Soc...

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International Conference on Computer and Information technology (ICCIT)

作者： Sovon Chakraborty Muhammad Borahn Uddin Talukdar Muhammed Yaseen Morshed Adib Sowmen Mitra Md. Golam Rabiul Alam Department of Computer Scinece and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Department of Artificial Intelligence Hebei University of Technology Beijing China

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%.

关键词： Chaos Economics Sentiment analysis Analytical models Social networking (online) Computational modeling Deep architecture

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Apache Kafka on Big data Event Streaming for Enhanced data Flows

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： K Padmanaban T. R. Ganesh Babu K. Karthika Balachandra Pattanaik Dhanabhavithra K C. Srinivasan Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KCG College of Technology Chennai Tamil Nadu India Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics.

关键词： System performance Pipelines Semantics Refining Distributed databases Transforms Big data data processing Throughput Real-time systems

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An improved Convolutional Neural Network model for human posture detection

An improved Convolutional Neural Network model for human pos...

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Chinese Control Conference (CCC)

作者： Xin Guo Jian Liu Yaoxu Lei School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China National Research Center for Rehabilitation Assistants Qinhuangdao Research Institute Qinhuangdao Hebei China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China

The classic OpenPose convolutional neural network model has excellent performance in the field of human posture detection. But it also has some shortcomings, such as poor accuracy and low accuracy of human posture detection partly. An improved OpenPose network model is proposed to solve these problems. First of all, the feature extraction network VGG -19 of OpenPose model is replaced by the residual network (ResN et) with residual learning structure to improve the training accuracy of the network. Secondly, at the same stage, the dual-branch parallel structure is changed to the single-branch serial structure to predict the part confidence maps (PCMs) and the part affinity fields (PAFs) of human joint points. Finally, the structure of the convolution kernel is optimized and the residual network is introduced. The accuracy of the algorithm is improved and the computational speed of the model is guaranteed as much as possible. Experimental verification on MPII data sets shows that the detection accuracy of the improved algorithm model in this paper can reach 79.5%, which is 3.9% higher than the original model. At the same time, it has a higher accuracy of detection compared with other human posture detection algorithms. It can be applied to human posture detection with a high pursuit of the accuracy of detection of the model, such as the field of intelligent driving and rehabilitation training and so on.

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Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design

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Cell Reports 2025年第6期44卷 115763页

作者： Zhu, Xinyuan Lu, Jiadong Hu, Xinting Jin, Tengchuan Lu, Shan Feng, Fuli School of Information Science and Technology University of Science and Technology of China Anhui Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China Department of Cellular and Molecular Medicine University of California San Diego San Diego CA United States

Predicting the binding of peptides to human leukocyte antigen (HLA) alleles is critical for identifying epitopes initiating immune responses in vaccine development and immunotherapies. Despite the progress in predicting binding peptides of 1% HLA class-I alleles with in silico prediction tools, it remains challenging to predict binding peptides for alleles lacking epitopes. To achieve the binding prediction on those zero-shot alleles, we developed a hierarchical progressive learning (HPL) framework that progressively learns sequence patterns of specific peptide-HLA complexes. HPL models improve the prediction performance for zero-shot HLA class-I alleles by 60.8% (1,414.0% for non-classical ones). We further developed an automated peptide mutation search program, which automatically identifies binding peptides for any target HLA class-I allele and mutates weak-binding or non-binding peptides to enhance binding affinity under the guidance of HPL models. Our program generated high-affinity binding candidates of any target allele with limited mutations in more than 38.1% of testing cases. © 2025 The Author(s)

关键词： CP: Immunology HLA-peptide binding immunotherapy protein language model

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Design and Implementation of Robust Multicast Trees for Lossy Wireless Ad-Hoc Networks

Design and Implementation of Robust Multicast Trees for Loss...

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International Conference on Computing and Networking technology (ICCNT)

作者： M. Preetha Jagmeet Sohal B.R Sampangi Rama Reddy Nakul S. Sharma S. Hemelatha Manpreet Singh Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper affords a layout and implementation of a sturdy multicast tree technique for loss wireless advert-hoc networks. The proposed technique seeks to provide dependable delivery of statistics in dynamic environments where hyperlinks revel in excessive degrees of packet loss. This method is based on a mixture of spanning tree clustering, hyperlink-country routing, and acknowledgements-primarily based recuperation mechanisms, using re-transmissions and adaptive retransmission periods to catch up on packet loss. The usage of these combined techniques permits for minimal deterioration of verbal exchange performance because of packet losses, whilst retaining a green network layout. Simulations had been conducted to evaluate the overall performance of the proposed method in assessment to previous works. The effects showed that the proposed method outperformed the other techniques in terms of multicast message transport fee. The proposed approach is also proven to reduce overhead site visitors because of retransmissions while as compared to previous works. The proposed method affords a suitable and efficient solution for reliable multicasting in loss wireless ad-hoc networks.

关键词： Wireless communication Uncertainty Layout Packet loss Throughput Routing Ad hoc networks Robustness Hypertext systems Wireless fidelity

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