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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Rithani, M. Kumar, Mahendira S. Kumar, Sudeesh V. Kumar, Praveen S. Priyadharshan, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information and Technology SRM Institute Of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798350374933

In the rapidly shifting workplace environment, fostering an inspiring and dynamic atmosphere is essential for organizational success. This paper introduces an innovative Workforce Enhancement Platform designed to improve transparency, teamwork, and communication within the team. The platform employs state-of-the-art techniques for analyzing textual data, utilizing Gradient Boosting Machines (GBM) such as XGBoost, LightGBM, and CatBoost, to evaluate and categorize worker-submitted issues. It features a comprehensive data cleaning procedure to remove extraneous elements like markup and special symbols, followed by converting the processed text into actionable features using methods such as term frequency and inverse document frequency. Central to the system is the application of GBM algorithms, which detect and classify the emotional tone of the reports, distinguishing between positive and negative sentiments. Furthermore, the platform incorporates advanced data visualization tools to provide an in-depth analysis of model performance, including accuracy metrics, confusion matrices, and feature importance evaluations. By optimizing the identification and classification of workplace issues, the platform facilitates effective resolution, fostering a setting of innovation and teamwork. The main goal of the platform is to boost staff engagement and support by addressing challenges proactively, thereby promoting both individual growth and collective progress. This comprehensive approach ensures that team members’ feedback is acknowledged and valued, ultimately contributing to a more positive and productive organizational environment. © 2024 IEEE.

关键词： Empowerment of personnel

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Intelligent Techniques for Nutrition Level Prediction in Guava Plants

Intelligent Techniques for Nutrition Level Prediction in Gua...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Nadiger, Rakshith S Suresh, Gautham Shobha Rani, N. Prabhu, Akshatha Sangamesh, M.A. Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Mysuru India Gitam University Gitam School of Technology Department of Computer Science & Engineering Bengaluru India National Institute of Engineering Department of Chemistry Mysuru India

ISBN: (纸本)9798350361537

Guava, a valuable tropical fruit crop, faces significant defect challenges due to diseases. Manual disease checks are time- consuming and error prone, leading to delayed action. Our research focuses on image processing to quickly and accurately identify guava diseases using deep learning approach with the help of Guava leaf images. This automation minimizes human intervention, enhances accuracy, and aids disease prevention. Early detection of these diseases improves crop quality and sustainability while reducing pesticide use. This Deep learning- driven approach aims to benefit farmers, consumers, and global food security by advancing guava farming practices. By using the YOLOv8 object detection model for 6 classes, we got an accuracy of 87.97%. © 2024 IEEE.

关键词： Agricultural Sustainability Deep Learning Guava Plant Disease Detection Nutrition level prediction YOLOv8

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Prohibited Object Detection using YOLOv7 on X-Ray Images of Airport Luggages (OPIXray) 14

Prohibited Object Detection using YOLOv7 on X-Ray Images of ...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Deena, S. Jayaprakash, L.S. Sudharson, S. Amrita Vishwa Vidyapeetham Computer Science and Engineering Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350335095

Ensuring airport security and reducing risks for passengers safe travels is crucial. Conventional security inspection methods involve manual inspection of passengers luggage using X-ray scanners to identify any prohibited items. However, this manual process is prone to human error and can be time-consuming. To address these challenges, there is a need for automated security inspection systems that can enhance efficiency and accuracy. Deep learning techniques, particularly in the field of computer vision, offer potential solutions for autonomous security inspection. One such model is YOLO (You Only Look Once), a real-time object detection system capable of identifying multiple objects in images or video streams. This makes YOLO an ideal choice for airport security applications that require both speed and accuracy. This paper proposes a method for the automatic detection of prohibited objects in X-ray images, aiming to identify potential threats such as weapons, explosives, and other prohibited items in real-time. The proposed system was evaluated by conducting extensive tests on the publicly available benchmark dataset known as OPIXray. This dataset consists of labeled X-ray images that contain prohibited objects. The key component of our method involves the utilization of the YOLOv7 network, which was trained on a subset of the OPIXray dataset. The study demonstrates the effectiveness of this approach in detecting and recognizing prohibited items in OPIXray image datasets. The proposed methodology achieved a precision of 0.73, recall of 0.76, mAP@0.5 of 0.491, and mAP@0.5:0.9 of 0.397 on the OPIXray dataset. The experiments demonstrate the model's high accuracy and speed, thereby indicating its suitability for real-world airport security applications. Comparisons with other methods show that our model outperforms in terms of accuracy and speed. This paper presents a high-accuracy method for real-time airport security applications utilizing the YOLOv7 network. © 2023

关键词： Airport security

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Biotic Stress and Severity Classification of Pear Leaves Disease with Ensemble Learning

Biotic Stress and Severity Classification of Pear Leaves Dis...

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2024 IEEE International Conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Sugunadevi, C. Maheswari, B. Uma Cmr Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350390759

Leaf disease frequently arises as pears plants mature. With a particular emphasis on categorizing the pear leaf disease, the proposed research work suggests the implementation of the CNN architecture that we use in this study to create this disease identification and classification method comprised of ResNet50, InceptionV3, VGG16, and VGG19. These neural networks have been pre-trained on the ImageNet dataset. The ensemble methodology that we describe produces final predictions for the test samples based on the recall, precision, accuracy, and f1-score scores of four standard evaluation metrics. Even though there is an increase in the amount of accuracy when the models are ensembled, the remarkable combination is InceptionV3 with VGG16 with an accuracy of 77.25%, precision of 79.34%, recall of 77.25% and F1 score of 76.50% and with the severity classification as accuracy 45.29%, precision 46.88%, recall 45.29% and F1 score 45.50% respectively. © 2024 IEEE.

关键词： Plant diseases

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Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model

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computer Modeling in engineering & sciences 2023年第10期137卷 903-922页

作者： Guangxia Xu Lei Liu Jingnan Dong Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Software Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code ***,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at *** this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent *** downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their *** experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.

关键词： Smart contract pre-trained language model deep learning recurrent neural network blockchain security

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Vehicle Insurance Damage Estimation Using YOLOv5 4

Vehicle Insurance Damage Estimation Using YOLOv5

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India Department of Computer Science and Engineering Gitam School of Technology Bengaluru Off-Campus Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Accident assessments for insurance companies are currently conducted manually, which can be time-consuming and prone to human error. The proposed method seeks to automate this process, aiming to save time and enhance accuracy. This paper introduces an innovative approach to analyzing object damage by leveraging the advanced YOLOv5 (You Only Look One Level) deep learning architecture. The research is meticulously crafted to address the imperative need for robust and precise detection and classification of damaged objects in images, crucial for effective assessment of structural and environmental integrity. Leveraging the unparalleled real-time object detection capabilities of YOLOv5, the framework is finely tuned and trained on a comprehensive dataset encompassing diverse scenarios of damaged objects. Through exhaustive experimentation and validation, our methodology demonstrates substantial advancements, offering unparalleled efficiency and accuracy in the domain of object damage analysis. The findings of this study hold significant implications for a broad spectrum of applications, ranging from infrastructure monitoring to disaster response, underscoring the transformative potential of deep learning in enhancing critical assessment processes. © 2024 IEEE.

关键词： Deep learning

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ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Fortaleza: The Emergence of a Network Hub

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arXiv 2023年第1期3卷 272-279页

作者： Bragion, Eric Akter, Habiba Kumar, Mohit Xu, Minxian Abdelmoniem, Ahmed M. Gill, Sukhpal Singh School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Jalandhar India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. © 2023, CC0.

关键词： Cloud data centre Latency Network hubs Fortaleza Roubaix Networking

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