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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, V. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

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SN computer Science 2024年第8期5卷 1-15页

作者： Sethupathi, M. Gowtham Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India

With the rapid increase in Electric Vehicles (EVs), the issue of charging them has received a lot of attention. Since one EV’s charging time cannot be cut quickly and the number of charging stations will not expand quickly, scheduling EV charging operations in metropolitan areas becomes vital as it can boost charging efficiency and minimize EV users’ concern over charging. There isn’t currently a useful scheduling software product available in this industry. Effective trip time prediction is essential for charging stop scheduling and route optimization, especially for EVs attempting to maneuver through intricate metropolitan *** order to precisely predict trip durations for EVs, this research presents a unique hybrid model that combines the Reinforcement Learning (RL) and Improved Weighted Wolf Optimization (IWWO) algorithms. The model's capacity to identify complex patterns in traffic data is improved by optimizing the selection of pertinent features and parameters via the use of the IWWO algorithm. The model's performance is then adjusted using RL approaches, which use iterative learning and adaptability to changing traffic circumstances. The suggested hybrid approach provides reliable and flexible EV travel time estimate by using the advantages of both optimization and machine learning paradigms. The outcomes of the experiments show how well the suggested method works in estimating trip times, especially in situations with erratic traffic patterns and variable ambient factors. With the use of this hybrid model, EV travel time prediction has advanced significantly, enabling more effective route planning and charge management to promote sustainable urban transportation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Charging management Electric vehicle Improved weighted wolf optimization Reinforcement learning Route optimization Sustainable transportation Traffic patterns Travel time estimation Urban mobility

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Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

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SN computer Science 2024年第8期5卷 1-22页

作者： Singh, Abhijeet Pratap Chaurasia, Brijesh Kumar Tripathi, Ashish Department of Information Technology Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

A revolutionary approach for enhancing driving efficiency and safety in intelligent transportation systems (ITS) is deploying autonomous vehicles. Vehicle-to-everything (V2X) technology facilitates interactions between vehicles and infrastructure. However, the Internet of Vehicles (IoV) is susceptible to many cyberattacks, encompassing impersonation, denial of service (DoS), and fuzzy assaults. This paper proposes an intelligent network intrusion detection system (NIDS) using machine learning algorithms. The usage of the ML approach in a Stacking-enabled Ensemble Learning-based Intrusion Detection Scheme (SELIDS) for IoV is proposed. We additionally examine each technique’s shortcomings and how they affect the NIDS efficiency. Deploying the proposed NIDS on the benchmark dataset demonstrates the capacity of the system to recognize different kinds of assaults. Finally, we explore the potential for NIDS to collaborate with additional security technologies in the future. Empirical results prove the efficacy of the proposed mechanism. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attacks Intelligent transportation systems (ITS) Internet of vehicles (IoV) Machine learning (ML) Network intrusion detection system (NIDS)

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Improving the Performance of Firewalls through Network Traffics Logs' Classification of Firat Dataset Using Decision Tree Algorithm 10

Improving the Performance of Firewalls through Network Traff...

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10th International Conference on Web Research, ICWR 2024

作者： Talaeepoor, Maryam Soleimani Neysiani, Behzad Jezi, Mohammad Davarpanah Naghshe Jahan Institute of Higher Education Faculty of Computer Engineering Department of Computer Engineering Isfahan Iran Department of Software Engineering Isfahan Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350394986

As long as a computer system is connected to the Internet, it is susceptible to attack as a victim. In computer networks, it becomes important to manage the network based on parameters such as network size and network data. Firewalls are devices that help network administrators in this case to establish security in the network, and can be based on the rules that the firewall is based on. It is configured to control incoming and outgoing network traffic. Firewalls can be considered the most vital components of the network in establishing security. Firat University introduced a dataset containing firewall logs with multiple classes for firewall decisions. This study uses data mining techniques to improve the validation performance of classification using various machine learning algorithms like neural networks, deep learning, and kNN. The experimental results show more than 10% improvement according to precision and recall rates among various folding scenarios used in related works with minor improvement in accuracy, too. The decision tree algorithm is fast and explainable versus other algorithms. © 2024 IEEE.

关键词： Classification (of information)

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Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

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computer Systems Science & engineering 2023年第6期45卷 3005-3021页

作者： K.Anuratha M.Parvathy Department of Information Technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia Department of Computer Science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

The COVID-19 pandemic has become one of the severe diseases in recent *** it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the *** public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those *** topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity *** research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 *** the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July *** extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is *** classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high *** identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been *** of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%neg

关键词： Twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare

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An Automated System Pre-Trained to Identify Cardiovascular Disorders from ECG Pictures 2

An Automated System Pre-Trained to Identify Cardiovascular D...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Efficacy of image based deep learning CNN models for bolt loosening detection in steel structures

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Asian Journal of Civil engineering 2025年第6期26卷 2409-2417页

作者： Alapati, Mallika Ramesh Chandra, G. Abboju, Shivani Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

Bolted connections are integral components in steel structures and often are vulnerable to loosening due to cyclic loading and fatigue. Detecting bolt loosening in early stages is critical to prevent sudden catastrophic failures. Recent advancements in image capturing and processing, combined with machine learning techniques have significantly improved the accuracy of detecting bolt loosening. Deep learning techniques, in specific those based on image analysis, have proven to be effective tool for damage detection in structural members. In this study, initially captured dataset of 120 images from a laboratory model is augmented to 576 images using the Roboflow platform. Investigations in this work are tied to one image data set and the metrics are analysed for how well a model performs. Usually, it depends on each use case as to which model would be best suited. In the present work, dataset featured two states of bolt loosening: tight and loose. Convolution Neural Network architectures AlexNet and ResNet are applied individually to the augmented dataset to predict the bolt loosening states as binary classification problem. The results demonstrated an accuracy of 87.931%, indicating that the AlexNet model effectively distinguished between loose and tight conditions. Whereas the ResNet algorithm exhibited relatively better accuracy of 90.8%. Further, an integrated hybrid model (CNN model with LSTM) is also applied on the same image data to further enhance the efficiency of the deep learning frame work. The performance of the trained CNN algorithms was further validated with image responses from various real time scenarios of various lighting conditions and found to be fairly good. This study highlights the potential of using deep learning techniques, specifically AlexNet, ResNet and CNN with LSTM for accurately detecting bolt loosening, thereby contributing to the safety and reliability of steel structures. © The Author(s), under exclusive licence to Springer Nature S

关键词： AlexNet Bolt loosening Bolted connections Convolution neural network architectures Image based deep learning ResNet architecture

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Breast Cancer Histopathological Image Classification Using PIP-Net 1

Breast Cancer Histopathological Image Classification Using P...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Aryan Titoriya, Ankit Kumar Singh, Maheshwari Prasad SRM Institute of Science &Technology Computer Science and Engineering Chennai India National Institute of Technology Computer Science and Engineering Patna India

ISBN: (纸本)9798350365016

Histopathological images serve as pivotal assets within the domain of breast cancer diagnosis, demanding profound comprehension for precise interpretation. This paper introduces a histopathological image classification framework employing the Patch-Based Intuitive Prototype Network (PIP-Net). PIP-Net finds important features without the need for manual part annotations by using prototype patches and self-supervised learning mechanisms. Consequently, PIP-Net adeptly discerns novel patterns within images, thereby facilitating informed therapeutic interventions. Leveraging the 'BrakeHis' dataset, encompassing histopathological images of breast cancer, this paper evaluates PIP-Net's efficacy across varying magnification levels, specifically at 40x, 100x, 200x, and 400x. The classification accuracies achieved are 98.49%, 94.53%, 99.33%, and 98.08% for each respective magnification level. The integration of prototypical elements within images serves to enhance correlation with human visual perception. This shows the paper's dual commitment to not only attaining superior classification performance but also aligning with interpretability and intuitive principles integral to medical image analysis. © 2024 IEEE.

关键词： Self-supervised learning

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