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6th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Kamran, Yashfa Tauqeer, Mahnur Ahmad, Moiz Tauqeer, Syed Muhammad Hamza National University of Science and Technology Department of Electrical Engineering Islamabad Pakistan Tum School of Engineering and Design Technical University of Munich Munich Germany University of Engineering and Technology Department of Electronics Engineering Taxila Pakistan

ISBN: (纸本)9798350389692

With the rise of Deep learning and Machine Learning, immense breakthroughs in the area of agricultural automation have now become possible. Most importantly, it is the kind of fruit ripeness that may be the main issue as it touches both the retailing and farming sectors. In this paper, Convolutional Neural Network (CNN) approach and 2-Dimensional Convolution (Conv2D) model is applied as a main part of differentiating between ripe and unripe fruits because of their main distinguishing feature. The model is refined through the use of image processing techniques like resizing and normalization, which make its generalization ability more robust. Using the Adam optimizer and the binary cross entropy loss function, the model is tuned for the binary classification task which we deal with. The results of the model can be observed through fruit maturity classification with training accuracy 99 percent and validation accuracy reaches 84 percent, Area Under curve (AUC) equals 0.92. © 2024 IEEE.

关键词： Fruits

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STUDY ON BENDING PERFORMANCE OF COLUMN-TO-BEAM CONNECTION WITH SAO-SHACHI JOINT FOR TRADITIONAL WOODEN BUILDINGS

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AIJ Journal of technology and design 2024年第76期30卷 1257-1262页

作者： Urakubo, Mikiya Miyamoto, Mitsuhiro Utsunomiya, Naoki Nakao, Masato Ohashi, Yoshimitsu Kita Shigenori Structural Design Office Co. Ltd Japan Faculty of Engineering and Design Kagawa Univ. Japan Dept. of Architectural System Technology Kinki Polytechnic College Japan Dept. of Architecture and Building Science Yokohama National Univ Japan Tokyo City Univ Japan

Sao-shachi joint is used for the joint at column-to-beam connections with large cross-sectional areas, providing relatively high resistance and exerting a significant impact on the overall building load-bearing capacity. Therefore, the seismic performance evaluation method for column-to-beam connections with Sao-shachi is important in designing traditional wooden buildings. This study aims to develop a formula for evaluating the bending performance of such joints based on full-scale experiments. The comparison of estimated values obtained from the proposed method with experimental results generally revealed accurate predictions of failure modes and moment and rotation angle relationships. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Wooden buildings

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Air-Ground Collaborative Control for Angle-Specified Heterogeneous Formations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Chen, Liangming Xiao, Jiaping Teo, Clarence Wei Rui Li, Jianqing Feroskhan, Mir School of System Design and Intelligent Manufacturing and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China school of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore College of Communication Engineering Hangzhou Dianzi University Hangzhou China

The need for better search and rescue capabilities has led to the increased demand for collaborative aerial-ground multi-robot deployments. However, most existing solutions require high communication bandwidth or bulky sensor equipment that is not suitable especially for aerial robots. To make the execution of these tasks more efficient, this paper proposes an angle-specified heterogeneous leader-follower formation framework consisting of unmanned aerial vehicles (UAVs) flying in a 3D space and ground robots moving in a 2D plane. The UAVs are controlled to maintain a desired angle-specified formation with the first three UAVs as the leaders forming a triangular shape and the remaining UAVs as the followers. The follower UAVs only need direction measurements to track the leader UAVs. For the ground robots, two leader robots track the UAV group and determine the orientation and scale of the ground formation, while the follower robots track the leader robots using only direction measurements. The proposed heterogeneous formation framework is energy-efficient as most robots only require low-cost and lightweight direction measurements. The stability of the proposed formation control algorithms is proved and validated through various physical application experiments. IEEE

关键词： Multi agent systems

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Wu, Guotong Nan, Yang Shang, Ke Ishibuchi, Hisao Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC. © 2024 IEEE.

关键词： Neural network models

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, systems and Information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Tamper-forgery Image Detection in Security Digital Image: A Review 3

Tamper-forgery Image Detection in Security Digital Image: A ...

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Thorat, Nilesh N. Dhotre, Prashant Kulal, Nilesh Pimpalkar, Aarti P Patil, Vijaya S. Kolekar, Sachin M. Mit Art Design and Technology University Mit School of Computing Computer Science & Engineering Department Pune India Mit Art Design and Technology University Mit School of Computing Information Technology Department Pune India

ISBN: (纸本)9798350354348

Concerns with image security exist in every sector that uses digital photographs. Historically, public safety and forensics have depended on images from the crime scene, biometric photos, suspect photos, and other sources. With the development of digital imaging, there has been a significant increase in the use of digital images in this field. Although the use of digital image processing has led to the creation of several new forensic methods of inquiry, it has also simplified picture modification. Digital picture validity is an issue since several snipping and image alteration programs are freely available to the public. It serves as solid proof in many different types of crimes and is utilized as documentation for many purposes. Creating and modifying photos has become easier and more accessible with the development of image manipulation and editing tools. The two most common methods of photo counterfeiting are copy-and-move forgery and image splicing. An image can be spliced, or combined from two photos, to hide or show an error scenario. A section of a picture can be copied and pasted into another image. The many kinds of digital image frauds are examined in this study along with software that detects them. An analysis was done on the methods currently in use for spotting phony photos. © 2024 IEEE.

关键词： Anonymity

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Post-Quantum Cryptography: Securing Critical Infrastructure Against Emerging Quantum Threats 4

Post-Quantum Cryptography: Securing Critical Infrastructure ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Hingoliwala, Hyderali Uttarkar, Amit Shendkar, Bhagyashree D Lokare, Deepali Sontakke, Palash Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India Mit Art Design and Technology University Mit School of Computing Department of Information Technology Pune India

ISBN: (纸本)9798331518981

The rapid advancements in quantum computing pose significant threats to traditional cryptographic systems, which are foundational to securing modern digital infrastructure. Cryptosystems like RSA and ECC, currently relied upon for secure communications, are vulnerable to quantum attacks, necessitating the development of post-quantum cryptography (PQC). The proposed methodology in this paper focuses on safeguarding critical infrastructure from quantum-based cyberattacks by leveraging PQC. The framework integrates several key strategies: hybrid cryptography, which combines classical and post-quantum algorithms to ensure security even as quantum threats evolve;quantum key distribution (QKD), ensuring secure communication by detecting any interception attempts;and post-quantum algorithms designed to resist quantum computing capabilities. The methodology also includes a robust security layer with incident response and threat monitoring systems to detect anomalies and initiate rapid responses to potential attacks. This holistic approach secures vital sectors like healthcare, energy, and telecommunications by future-proofing cryptographic measures against quantum threats. © 2024 IEEE.

关键词： Quantum computers

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Dynamic Scheduling of Operators in an Unbalanced Assembly Line Based on Weighted Fuzzy Petri Nets Decision

Dynamic Scheduling of Operators in an Unbalanced Assembly Li...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Tang, Delian Wang, Junfeng Tang, Xia School of Mechanical Science and Engineering Huazhong University of Science and Technology Department of Industrial and Manufacturing System Engineering Wuhan430074 China

ISBN: (纸本)9798350323153

The production tasks of aerospace enterprises are increasing and the order demands are variable. The production flexibility, production capacity and efficiency of aerospace products are gradually enhanced. Usually, because of unbalanced process time of works, the operators in aerospace products assembly line are not belonged to a fixed workstation. For dynamic scheduling of limited operators in shift-based assembly lines of this paper, the knowledge of scheduling decisions for multiple dynamic changes in the assembly line is analyzed, and a decision model based on a weighted fuzzy Petri net is proposed, which can reasonably allocate the limited operators to different assembly stations in order to improve the assembly efficiency. A case study is given to shown the effect of the operators dynamic scheduling strategy on the throughput improvement of the assembly line. © 2023 IEEE.

关键词： assembly line fuzzy Petri net Operator scheduling unbalanced process time

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Sentirec - A Sentiment Analysis with Recommendation system 8

Sentirec - A Sentiment Analysis with Recommendation System

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8th International Conference on Communication and Electronics systems, ICCES 2023

作者： Oswalt Manoj, S. Catherine Chandralekha, T. Dharshini, R.M. Hemadharsini, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

The standard language is assessed, and the feelings transmitted by the individual are brought up. The purpose of sentiment analysis is to determine the polarity of a person's textual opinion. Most of the people use social media to market ourselves and share all knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been studied by several internet companies that offer materials based on human emotions expressed through social media. Many people use social media to post and share their thoughts and feelings. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has an intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related content to viewers based on their current statement or issue. The algorithm also suggests customized video sets to users based on their social media activity, such as diverse perspectives on a particular social problem about which they actively express their thoughts on social media. NLP algorithms are utilized for sentiment analysis and recommendation systems. © 2023 IEEE.

关键词： Recommender systems

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