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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Security Metrics and Authentication-based RouTing (SMART) Protocol for Vehicular IoT Networks

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SN computer science 2024年第2期5卷 1-14页

作者： Kumar, Rakesh Singh, Sunil K. Lobiyal, D.K. Kumar, Sudhakar Jawla, Shashi Department of Applied Computational Sciences & Engineering GLBITM Greater Uttar Pradesh Noida India Department of Computer Science & Engineering CCET Panjab University Chandigarh India School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering SVIET Mohali India

Routing protocols, responsible for determining optimal paths, fall into two main categories: reactive and proactive protocols. In the realm of reactive routing protocols, exemplified by Ad hoc On-demand Distance Vector (AODV), routes are created only when there is an actual data transmission requirement. In contrast, proactive routing protocols maintain pre-computed paths to all potential destinations, resulting in reduced resource utilization within reactive protocols and continuous route maintenance within proactive ones. Reactive routing protocols are resource efficient as they establish routes as needed, while proactive counterparts maintain routing tables for all possible destinations, ensuring constant route availability regardless of data transmission demands. This paper primarily concentrates on the reactive routing protocol category, focusing on real-time path optimization and routing information updates. In the context of Vehicular Internet of Things (VIoT) networks, where malicious entities might attempt to flood, mislead, or impersonate routing packets, it is imperative to ensure robust security measures within the routing protocol. Unfortunately, secure routing protocols in VIoT networks, including AODV, SAODV, and SGHRP, often exhibit inefficiencies and impose a high overhead. To address these challenges, this research paper introduces the Security Metrics and Authentication-based RouTing (SMART) protocol for VIoT networks, with a focus on enhancing security while minimizing overhead. The SMART protocol utilizes the Merkle tree for hash (digest) generation, which is then encrypted using Elliptic Curve Cryptography (ECC) to reduce overhead. This proposed protocol enhances security by authenticating the source and incorporating security metrics into the routing information. To assess the performance of the SMART protocol, simulations were conducted using Network Simulator-2 (NS₂). The results demonstrated an improved packet delivery ratio, red

关键词： AODV Authentication ECC Hash digest Merkle Tree OBU Routing Protocol SAODV VIoT Networks

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Kathakali Mudras: An AI-Enhanced Predictive Approach 11

Kathakali Mudras: An AI-Enhanced Predictive Approach

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Interpretable Arabic Hate Speech Detection using Large Language Model 2

Interpretable Arabic Hate Speech Detection using Large Langu...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alwateer, Majed Gad, Ibrahim Elmarhomy, Mahmoud Elmarhomy, Ghada Hashim, Hasan Almaliki, Malik Atlam, El-Sayed College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Tanta University Faculty of Science Computer Science Department Tanta31527 Egypt College of Applied Taibah University Computer Science and Information Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331506995

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content. © 2025 IEEE.

关键词： Arabic Hate Speech Detection Large Language Mode XAI

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Tag Prediction of Stack Overflow Questions using NLP and ML 15

Tag Prediction of Stack Overflow Questions using NLP and ML

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Prasad, P. Yogendra Priyanka, Mylavarapu Amreen, Shaik Reddy, N. Desai Sreenivas Teja, Muppala Bhanu Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Technology is growing in great leaps and bounds and the number of innovations is increasing every day with major contributor being the advancement in the software development environment. programming environment includes plethora of tools and support platforms that gives a complete assistance to the programming designers. These platforms can be question- answer based or coding based or tutorial based. Stack Overflow is a very popular online question- answer platform in the current generation and is growing to be the greatest question-answer gathering for programmers and software developers. The amount of information in this forum is so huge that one of the main functionalities of this platform would be tagging i.e., identifying the label or class to which the piece of information belongs to. Tagging helps in easy retrieval and organization of data. We propose a multi label classification system that labels all the questions on the forums with their appropriate tags autonomously. This auto-labelling framework makes it very easy for the users to find answers for the inquiries posted, given significant tags for the same and also helps the site in organizing their questions in a better way. © Grenze Scientific Society, 2024.

关键词： Software design

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Stress Prediction using Machine Learning Algorithms 15

Stress Prediction using Machine Learning Algorithms

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sivaiah, B.V. Gayatri, Tangi Setti Umadevi, Thati Reddy, Rajula Partha Siva Sravya, Modiboina Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Many people nowadays are dealing with psychological trauma. referred to as stress. Stress is defined as "a state of tension and pressure brought on by any thought or incident that makes you feel anxious, agitated, or frustrated". Many people have experienced stress as a result the current situation, particularly youth and working professionals. These days, a rise in stress can cause a number of issues, including heart attacks, depression, and suicide. Many academics have turned their attention to stress identification as mounting data indicates that stress related health illnesses associated with the demanding modern lifestyle are becoming more and more common. Consequently, this study's main objective is to predict stress levels, using user-retrieved data that is then confirmed using a subjective stress scale. In this case, a ML algorithm should be trained on the data to forecast the stress level. With the help of our stress prediction, you can quickly determine your current stress level and steer clear of any unfavorable circumstances. With the help of this approach, people may proactively handle future problems and avoid unfavorable consequences by having correct insights into their stress levels. The project's ultimate goal is to improve mental health by providing a tool for efficiently determining and controlling stress levels and encouraging a better way of living. © Grenze Scientific Society, 2024.

关键词： Diseases

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Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosis

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Neural Computing and Applications 2024年第34期36卷 21383-21413页

作者： Zakariah, Mohammed Al-Razgan, Muna Alfakih, Taha Department of Computer Sciences and Engineering College of Applied Science King Saud University Riyadh11543 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Skin cancer poses a significant health hazard, necessitating the utilization of advanced diagnostic methodologies to facilitate timely detection, owing to its escalating prevalence in recent years. This paper proposes a novel approach to tackle the issue by introducing a method for detecting skin cancer that uses MixNets to enhance diagnosis and leverages mobile network-based transfer learning. Skin cancer has diverse forms, each distinguishable by its structural attributes, morphological characteristics, texture, and coloration. The pressing demand for accurate and efficient diagnostic instruments has spurred the investigation of novel techniques. The present study utilizes the ISIC dataset, comprising a validation set of 660 images and a training set of 2637 images. Moreover, the research employs a combination of MixNets and mobile network-based transfer learning as its chosen approach. Transfer learning is a technique that leverages preexisting models to enhance the diagnostic capabilities of the proposed system. Integrating MobileNet and MixNets allows for utilizing their respective functionalities, resulting in a dual-model methodology that enhances the comprehensiveness of skin cancer diagnosis. The results demonstrate impressive performance metrics, with MobileNet and MixNets models, and the proposed approach achieves an outstanding accuracy rate of 99.58%. The above findings underscore the efficacy of the dual-model method in effectively discerning between benign and malignant skin lesions. Moreover, the present study aims to examine the potential integration of emerging technologies to enhance the accuracy and practicality of diagnostics within real-world healthcare settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Diseases

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