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2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Swetha, K.R. Hemadarshini, M.P. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (纸本)9798350388602

It is essential to comprehend medicinal plant species in order to safeguard and preserve biodiversity. These plants require complex, intensive processes for their classification and identification, which are usually overseen by botanical specialists. The goal of this systematic review is to provide a comprehensive assessment of earlier studies on the use of deep learning methods for the identification and categorization of species of medicinal plants. By revolutionizing plant classification, deep neural networks (DNNs) have made substantial advancements in crop yield protection and agricultural risk management. Eight therapeutic plants are recognized and categorized in the suggested system with the use of DNN models, and important assessment metrics including F1 score, recall, accuracy, and precision are also calculated. This method produces subtle insights that make it easier to distinguish between therapeutic plants. © 2024 IEEE.

关键词： Plant diseases

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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AI-Driven Grading of Mango Quality Using External Characteristics: A Machine Learning Approach

AI-Driven Grading of Mango Quality Using External Characteri...

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2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Manu, Y.M. Priyanka, M.E. Adichunchanagiri University Bgs Institute of Technology Department of Computer Science & Engineering BG Nagara Mandya India

ISBN: (纸本)9798350388602

Right now, a lot of subjective human judgment goes into classifying mangoes and other fruits, especially when it comes to poor productivity, which results in less than ideal classification accuracy. Our work suggests a novel method for assessing fruit quality that takes into account both weight and exterior features in order to address this. Mangoes are graded into three groups, G1, G2, and G3, using machine learning approaches, namely Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). These models make use of a variety of input features, transforming unstructured visual data into structured datasets for study. After preparation, the data are tested and trained to provide reliable classification results. © 2024 IEEE.

关键词： Adversarial machine learning

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Design Process and Development of Automation for Smart Buildings 1

Design Process and Development of Automation for Smart Build...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sethi, Gunjan Khurana, Deepika Verma, Monika Pant, Surmadhur Chauhan, Priyanka Bhatt, Bhaskar Department of Computer Science and Engineering Delhi Technical Campus Uttar Pradesh Greater Noida India IIMT College of Engineering Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Shivalik College of Engineering Department of ECE Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat Almora India

ISBN: (纸本)9798331518578

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure. © 2025 IEEE.

关键词： Smart homes

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UPI Based Financial Fraud Detection Using Deep Learning Approach

UPI Based Financial Fraud Detection Using Deep Learning Appr...

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2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Gupta, Vaishali Sharma, Sahil Nimkar, Suhani Pathak, Suhani Institute of Engineering & Science Ips Academy Dept. of Computer Sc. & Engg. Indore India

ISBN: (纸本)9798350388800

This research seeks to tackle the essential issue of fraud detection in view of the increasing challenges presented by the changing landscape of online transactions, particularly inside the Unified Payments Interface (UPI). The research deviates from the usual methods used in this field and adopts a fresh approach by using Recurrent Neural Network (RNN) as an advanced instrument for detecting fraudulent financial transactions. This break with tradition shows that people have now come to terms with the fact that traditional methods have their limits and that CNN and RNN have something special to convey. By applying the algorithm in UPI transaction dataset, we segregate the fraud and legitimate transaction. To evaluate the proposed approach, we applied the test data on confusion matrix and got the True Positive Rate (TPR) is 87.5%, and the False Positive Rate (FPR) is 13.4%. © 2024 IEEE.

关键词： Decentralized finance

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Predictive Modeling of Soil Fertility Grades Using Random Forest Algorithm

Predictive Modeling of Soil Fertility Grades Using Random Fo...

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2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Gupta, Sanket Goud, Amit Jain, Priyansh Darshni, Mehul Medi-Caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

This paper presents an coordinates system tackling machine learning (ML) strategies to improve rural efficiency and maintainability. It presents a novel Edit Choice Strategy (CSM) pointed at maximizing regular trim abdicate rates to drive financial development. Besides, the paper examines the execution of a shrewdly trim proposal framework, named 'Develop Well,' encouraging ranchers in selecting fitting crops based on natural variables, complemented by a Precipitation Indicator module for precise precipitation determining. Also, the investigate traces the improvement of a abdicate expectation framework utilizing real-time rural meteorological information and soil behavior investigation to figure edit yields successfully. Through these activities, the consider offers a comprehensive procedure to optimize agrarian yield and advance sustainability. © 2024 IEEE.

关键词： Adversarial machine learning

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Feedback Control Based on Trust for Varied Automation Transparency 1

Feedback Control Based on Trust for Varied Automation Transp...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Bandyopadhyay, Krishnan Banerji, Nandan Mishra, Ishani Kandpal, Ankit St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Karnataka Bengaluru India Sikkim Manipal Institute of Technology Sikkim Manipal University Department of Computer Science and Engineering Sikkim India New Horizon college of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Interactions between people and automation depend heavily on human confidence in the latter. Lack of trust can cause people to not use automation, while too much trust can cause them to put their faith in a flawed autonomous system, which could have detrimental effects on them. Transparency and trust in automation are vital for the usefulness and security of human-robotization frameworks. The majority of the research that has been done on the trust that human operators have in robotics and automation has been an ongoing variable that has received little attention. paid to how trust changes over time. In order to fill this research void, we offer a study that examines how trust is dynamic. Using real-time trust measurements, we first discovered options to quantify trust of entirety, which we described as a metric that considers trust throughout the entire human-automation interaction experience. In this study, a feedback control strategy for powerfully changing mechanization straightforwardness and transparency concurring on client trust is analysed. The proposed approach tweaks mechanical way of behaving and correspondence by incorporating an input control component with a trust assessment model. The adequacy of the feedback control framework in improving errand execution and trust adjustment under different levels of computerization straightforwardness and transparency is checked utilizing MATLAB reenactments. © 2025 IEEE.

关键词： Human robot interaction

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Evaluation of Classifiers Based on Neural Network for Handwritten Digit Recognition

Evaluation of Classifiers Based on Neural Network for Handwr...

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2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sehta, Neha Saraf, Krupi Vinchi, Aditya Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Handwritten Digit Recognition (HDR) refers to the computer' s ability to identify and categorize hand written digits from various sources into 10 predetermined categories, ranging from 0 to 9. The issue of digit recognition is significant and crucial. Due to variations in size, thickness, location, and orientation of manually written digits, several challenges need to be addressed in order to evaluate the problem of recognizing handwritten digits. The diversity and variety in the composition techniques of different persons also impact the pattern and appearance of the digits. It refers to the approach of recognizing and organizing written numbers. It offers a diverse array of uses, such as automated bank checks, postal addresses, and tax paperwork, among others. The objective of this research is to employ a classification system to accurately identify handwritten numerals. The consequences of the most widely employed deep learning algorithms, such as multilayered perceptron network, in conjunction with convolution neural networks, are being discussed. Comparison of five neural network classifier shows that accuracy up to 99% can be achieved only in few epochs of training with default configuration of hyper-parameters. © 2024 IEEE.

关键词： Deep learning

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Automated Subject Allotment to Faculty in an Academic Institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

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2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sehta, Neha Shah, Naman Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty. © 2024 IEEE.

关键词： Automation

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Leveraging Data Efficient Image Transformer (DeIT) for Road Crack Detection and Classification

Leveraging Data Efficient Image Transformer (DeIT) for Road ...

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2024 international conference on advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Anzum, Humaira Sammo, Md. Nabil Sadd Akhter, Shamim Ahsanullah University of Science and Technology Aisip Laboratory Department of Computer Science and Engineering Dhaka1208 Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh

ISBN: (纸本)9798350350289

Accurate detection and classification of road faults such as cracks is critical for transportation infrastructure maintenance. Road cracks impede comfortable traveling, endanger passenger safety, and create incidents. In this paper, we propose a new Data Efficient Image Transformer (DeIT)-based Road crack identification system that can distinguish between cracks and non-cracks in road surfaces. This study also evaluates how well DeIT performs in comparison with YOLOv5, YOLOv8, Xception, ResNet50 and MobileNetV2 deep learning algorithms. We have used a publicly available dataset consisting of road images from Kaggle Concrete Data to train and test our model. For identifying cracked and non-cracked road surfaces, our proposed model achieved the highest 99.75 % accuracy on the test dataset using DeIT algorithm. © 2024 IEEE.

关键词： Deep learning

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