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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

引用

2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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A Comparative Study between Agile and Waterfall Methodologies during software Development Process 25

A Comparative Study between Agile and Waterfall Methodologie...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Saadeh, Rania Fawareh, Faid AlNoor Elrashidi, Ali Al-Shdaifat, Hazim M. Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This study aims to investigate and compare the using of waterfall and agile methodologies in the software development process. A survey were collected from 2017 to 2023 to investigate the use of waterfall and agile methodologies in software development. Moreover, the papers were classified based on published year, country, and used software development methods. In addition, the papers were summarized to highlight the state of the art of the most research investigate to comparison of using a waterfall and agile development processes. The research result discover that agile has shown the best results compared to other techniques for big software project furthermore agile requires small team members, also it shows acceptable result on small to medium projects, is more flexible to requirements changes, and software testing can be implemented after each sprint. The research result shows that waterfall methodology for small to medium projects is acceptable for small team. © 2024 IEEE.

关键词： Agile prototype software development Life cycle Water fall

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Respiratory Estimation During Sleep Using dept. Camera in Real Environment 13

Respiratory Estimation During Sleep Using Depth Camera in Re...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Kushida, Daisuke Kodani, Ryuga Tottori University Faculty of Engineering Dept. Electorical Engineering and Computer Science Tottori Japan Tottori University Graduate School of Sustainability Science Dept. Engineering Tottori Japan

ISBN: (纸本)9798350355079

Sleep apnea syndrome (SAS) is common but people are generally unaware of this condition, as it occurs during sleep. A method was previously proposed to estimate respiration during sleep in a non-contact and non-constraint manner using a dept. camera that is suitable for everyday use. The method was validated on subjects who were awake but remains untested under sleeping conditions. In this study, 12 men aged 65 years or older were tested under actual sleeping conditions for eight days. The effectiveness and accuracy of the proposed method were confirmed through a comparison with commercially available products. © 2024 IEEE.

关键词： Sleep research

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An Empirical Study on Measuring the Impact of Code Smells on Web Applications 25

An Empirical Study on Measuring the Impact of Code Smells on...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Aljawawdeh, Hamzeh Fawareh, Faid AlNoor Alshraiedeh, Fuad Khouj, Mohammed Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Code smells are a surface symptom that generally indicates a more serious problem in the software system therefore it is kind of hard to detect them manually so many automated tools are required to get the job done. The study focuses on proposing an empirical study that has been applied on web applications in order to detect the large classes manually based on Michele and Marinescu large classes' detection strategy as shown in Figure 1. The result of the study was the failure of the first project group due to the inability and agreement to identify large classes, while the second project group succeeded, as the participants agreed to define large classes more precisely, in addition to that the study showed that the process of manually identifying large classes is a very difficult process therefore an automated tools (AI tools) are required to make it easier and more accurate. © 2024 IEEE.

关键词： Application programs

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Proposal of Healthcare System for Pathological Symptom Detection Employing Voice Analysis

Proposal of Healthcare System for Pathological Symptom Detec...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Chenyang Liu, Jiang Shimamoto, Shigeru Dept. Computer Science and Communications Engineering Graduate school of Fundamental Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798350350548

Mobile healthcare has attracted significant attention in recent years as people have become more health-conscious. Furthermore, there is an increasing demand for non-contact and non-invasive diagnostic methods to reduce the risk of infection and pain. This study proposes a healthcare system that detects pathological symptoms by utilizing speech audio. Existing methods for the detection of COVID-19 have relied on cough recordings. To investigate the potential application of this method in the real use case, this study utilizes speech audio instead of cough recording. Our target is to alleviate the burden on the medical field and help medical workers by implementing this mobile healthcare system. This method uses transfer learning to address the problem of limited data. We evaluate the accuracy with transfer learning and random forest classification utilizing the specific features extracted in such a spectrum and spectrogram. © 2024 IEEE.

关键词： Audio recordings

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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Real Time Machinery Health Detection Via Thermal Imaging 10

Real Time Machinery Health Detection Via Thermal Imaging

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Khan, Jahiya Nene, Manisha J Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798331511890

Marine vessels are equipped with advanced and integrated complex machines that operate in challenging conditions, where the failure rate is high. One of the most vital elements of these machines is fault diagnosis and maintaining 100 percent operational states. Any machinery breakdown at sea during maritime operations can have severe consequences leading to operational impacts and reducing combat efficiency. The existing maintenance routines - PPM (Planned Preventive Maintenance) and CBPM (Condition Based Predictive Maintenance) are reactive approaches that may miss early signs of material degradation, leading to unplanned machinery breakdown at sea. An efficient method to counter this issue in real time is thermal imaging. The dataset required to develop a thermal prognostic model for real-time machinery health detection is not available. This paper presents a systematic approach to develop a prognostic model for real time machinery health detection via thermal imaging. We have developed a custom thermal image dataset representing various machinery conditions-normal, defective and degraded. We have developed our real-time prognostic model 'Thermal Prognostic Model'. This model targets fuel systems to prevent major fires and accidents at sea by predicting potential failures and taking corrective actions with mAP50 0.956, precision 0.945 and recall 0.957. © 2025 IEEE.

关键词： Predictive maintenance

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Frank, Anilloy Raj, Mrinal Presidency University Dept. of Electronics and Communication Engineering Bangalore India Presidency University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350309140

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation. © 2024 IEEE.

关键词： Child Emotional development face recognition Image processing Machine Learning in education

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Design of a Smart and Green Industry 4.0 Architecture 24

Design of a Smart and Green Industry 4.0 Architecture

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamdan, Khaled Kazar, Okba College of It Uaeu Dept. of Computer Science and Software Engineering United Arab Emirates College of It Uaeu Dept. of Information Systems & Security United Arab Emirates

ISBN: (纸本)9798350384307

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called 'Digital Factory', 'Industry 4.0'. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation. © 2023 IEEE.

关键词： environmental impact Industry 4.0 smart and green Industry 4.0

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