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Enhancing Road Safety: The Role of Intelligent Driver Drowsiness Detection Systems

Enhancing Road Safety: The Role of Intelligent Driver Drowsi...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shanu Khare Navjot Singh Talwandi Dept of Computer Science Engineering Chandigarh University Punjab India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

The analysis of this research paper is touching the road safety concerns by focusing on developing a system which catches the drowsy state of the person who is driving which emerges as a big reason that causes up to 30due to this. Throughout the paper, the study discusses about literature review and various methodologies that include physiological, behavioural, and multimodal fusion approaches showing the evolution and history of drowsiness detection technologies. The methodology includes using of various sensor technologies and algorithms to collect data from the driver and process it to analyse data in real time. Eye movement and facial expressions are extracted which helps the system to identify signs of drowsiness with greater accuracy. When the driver is detected as drowsy a mechanism is triggered to alert the driver, averting accidents. Even with some challenges, the system is a big step towards increasing road safety utilizing advanced technologies to reduce the risk of driver fatigueless.

关键词： Technological innovation Accuracy Psychology Road safety Real-time systems Physiology Reliability Vehicles Systematic literature review Accidents

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Renal Cell Carcinoma Classification: Deep Learning with MLflow, DVC, and AWS CI/CD Deployment

Renal Cell Carcinoma Classification: Deep Learning with MLfl...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： J Niveditha S Supreeth Kirankumari Patil dept of Computer Science and Engineering Reva University Bangalore India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

One prevalent type of kidney cancer that can be challenging to diagnose and treat is renal cell carcinoma (RCC). This article offers a novel method for applying deep learning techniques to the classification of RCC data. In particular, we examine the performance of three distinct models: ResNet50, VGG16, and a special 2D convolutional neural network (CNN). Our method makes use of cutting-edge machine learning frameworks like MLflow and DVC to streamline model building, experimentation, and version control. We describe an effective deployment pipeline that facilitates seamless integration with healthcare systems using AWS CI/CD infrastructure. The dataset, which includes 8,143 normal images and $\mathbf{1 0, 8 7 8}$ tumor images, is sourced from the Cancer Imaging Archive. CNN-4 was the classification model that obtained the highest accuracy, and CNN-6, ResNet50, and VGG16 were the detection models that achieved the highest accuracy, with respective results of $99 \%, 98 \%$, and 98%. These results demonstrate how our approach can be utilized to improve patient outcomes and early detection of RCC while simultaneously improving the accuracy of RCC diagnoses.

关键词： Deep learning Accuracy User interfaces Streaming media Convolutional neural networks Kidney Cancer Tumors Residual neural networks Software development management

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The Vibration Spectrograms as well as EL System for Assessing the Cutting Tool and Insert Condition 4

The Vibration Spectrograms as well as EL System for Assessin...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Radhakrishnan, C. Malhotra, Sonal Preethi, P. Subashini, R. Saravanan, T. Asokan, R. Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Trichy India Graphic Era Hill University Department of Cse Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India Kongunadu College of Engineering and Technology Dept. of Cse Trichy India Meenakshi Sundararajan Engineering College Department of Computer Science and Engineering Chennai India Dept. of Cse Bengaluru India Kongunadu College of Engineering and Technology Department of Ece Trichy India

ISBN: (纸本)9798350360165

In-process damage to cutting instruments can result in surface deterioration, leading to substantial financial losses. Detecting potential failures before they escalate to critical stages is vital, making Tool Condition Monitoring (TCM) indispensable. Previous research has predominantly focused on Machine Learning (ML)-based TCM, particularly Deep Learning (DL). To enhance the resilience of TCM frameworks in noisy cross-domain machining environments, a deeper understanding of deep neural networks, their hierarchical compositionality, and distributed modeling is necessary. This study delves into these intricacies and proposes an innovative approach using Ensemble Learning, combining CNN architectures such as AlexNet, ResNet-50, LeNet, Stand-5, and VGG-16. These architectures are applied to monitor real-time spindle vibrations associated with diverse milling cutter configurations, including both healthy and defective states. The collected data, transformed into time and frequency domains, was processed using graphical techniques, specifically spectrograms. The Ensemble Learning algorithm was meticulously trained, rigorously tested, and validated with diverse datasets, demonstrating promising results. This approach marks a significant advancement in creating a robust TCM framework capable of effectively identifying and mitigating damage in cutting instruments within complex machining environments. © 2024 IEEE.

关键词： Spectrographs

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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Amit Sharma Nitin Auluck Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments.

关键词： Machine learning algorithms Processor scheduling High performance computing Heuristic algorithms Supervised learning Machine learning Dynamic scheduling Prediction algorithms Real-time systems Edge computing

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G-TRETA A Novel Multi-Objective Task Scheduling Algorithm in Cloud Computing

G-TRETA A Novel Multi-Objective Task Scheduling Algorithm in...

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作者： K. M. S. U. Bandaranayake Sunimal Rathnayake University of Moratuwa Dept. of Computer Science and EngineeringSri Lanka

Cloud computing has revolutionized the IT industry by granting users to access computing resources and services over the Internet. Cloud offers an array of benefits like on-demand, scalable, cost-effective, and reliable resources and services to end users. This revolutionized technology enables various computing capabilities and data requirements to cloud users making it one of the most used computing models in the IT industry today. Task scheduling is considered an NP-complete problem. Genetic algorithms are widely used to find an optimal schedule for a task-scheduling problem with multiple objectives. None of the previous studies has considered the total resource execution time of the virtual machine as a scheduling parameter for finding a better schedule using metaheuristics like genetic algorithms. In this study, we propose a novel fitness function considering total resource execution time and cost in finding an optimum schedule. Hence we propose a novel multi-objective genetic algorithm G-TRETA with the above fitness function. The proposed algorithm was compared with FCFS, MaxMin, MinMin and MCT algorithms for the degree of imbalance, makespan, cost, and system throughput. CloudSim 5.0 simulator and DEAP framework with synthetic data traces and Nasa Ames/iPSC 860 real world traces were used to evaluate the algorithm. From the evaluation results the proposed algorithm gives better results. We can observe 36.37% and 50.46% for makespan, 57.16%, 101.88% for system throughput, 1.47 and 0.94 differences of index improvement for the degree of imbalance for synthetic and real world traces respectively while having efficient cost values.

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MURLi: A Tool for Detection of Malicious URLs and Injection Attacks 4th

MURLi: A Tool for Detection of Malicious URLs and Injection ...

引用

4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Devalla, Vihar Srinivasa Raghavan, S. Maste, Swati Kotian, Jaaswin D. Annapurna, D. Dept. of Computer Science Engineering PES University Karnataka Bengaluru560100 India

SQL databases have been a staple for storing data in enterprises to meet their database requirements. SQL injection has also become a major security threat due to the growth of SQL. Injection attacks occur when the query in the database is modified by malicious data from an unsecure user. The use of NoSQL databases has been growing steadily due to advancements in Big Data and similar domains, and as an alternative to SQL databases. But recent studies have proved that even NoSQL is susceptible to injection attacks. Malicious websites can harm a person using the internet in various ways. Phishing attacks, Spam attacks, Defacement Attacks can occur once a user clicks on a URL. In this research, we propose the tool mURLi, for early detection of Malicious URLs, SQLi and NoSQLi, which pose a large threat to the end-users. To build mURLi, we have used various deep learning models (ANN, BiLSTM, BERT) and machine learning models (Random Forest, XGBoost, Adaboost, KNN) in an attempt to detect these attacks (SQLi, NoSQLi and Malicious URLs) more accurately than traditional methods and have presented a comparative study of the same. The results show that BERT model outperforms the existing tools for SQL and NoSQL attacks with an accuracy of 99.6% and 99.01% respectively, which was achievable due to its pre-training and deep language knowledge. BiLSTM proves to be the best tool for detection of Malicious URL attacks, with the results being 95.2% accurate. © 2023 The Authors. Published by Elsevier B.V.

关键词： Adaptive boosting

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Machine Learning Approach for Skin Melanoma Classification

Machine Learning Approach for Skin Melanoma Classification

引用

2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Sanvordekar, Manjusha Kuppili, Venkatanareshbabu Modi, Chirag National Institute of Technology Dept. of Computer Science & Engineering Goa Ponda India

ISBN: (纸本)9781665454995

Melanoma is the most fatal type of malignant skin cancer, posing a serious threat to people's physical health. If melanoma of the skin is detected early, the chances of survival are very high. Dermoscopic images can be used to make an early diagnosis. Dermoscopy magnifies the skin, allowing dermatologists to better evaluate morphological features that are not easy to see with the naked eye. Machine learning is now an important technique for detecting various types of skin cancers. Convolutional Neural Network (CNN) has the potential to greatly assist dermatologists in the diagnosis of melanoma accurately. This paper uses a machine learning based approach to detect benign and malignant forms of skin cancer in dermoscopic images. The EfficientNet CNN model used here can design the appropriate network architecture to extract features with greater better accuracy and efficiency. The model is evaluated using dataset from the ISIC Archive. © 2022 IEEE.

关键词： Convolutional neural networks

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Community-Collaborative Solar Dryer Prototype: Development, Deployment, and Testing for Sustainable Food Preservation

Community-Collaborative Solar Dryer Prototype: Development, ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Lugnasin, Joseph Vincent B. Peña, Robert Alfie S. Dumlao, Samuel Matthew G. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Laboratory Dept. of Electronics Computer and Communications Engineering Quezon City Philippines Graduate School of Energy Science Kyoto University Department of Socio-Environmental Energy Science Kyoto Japan

ISBN: (纸本)9798350350821

In the Philippines, the prevalent method for drying agricultural and aquatic products like rice, corn, and fish relies on the traditional sun-drying technique where the produce is laid out under the sun, in an open area, and exposed to environmental factors like dust, dirt, and insects. This practice persists primarily because no other alternatives can guarantee a cost-effective yield when drying agricultural products. To address this, a multilevel solar dryer with solar-powered monitoring for households was developed as a sustainable option. Inputs from farming and fisherfolk communities were gathered through interviews and consultations, which served as the basis in designing the solar dryer. A prototype was built and then deployed in a fisherfolk household in Bani, Zambales. In this paper, three species of fish, locally known as bisugo, mataan, and danggit, were dried using the solar dryer. In parallel, traditional sun drying was conducted to compare the drying time of the two methods. Data showed that the multilevel solar dryer was effective in trapping the heat inside its drying chamber. Moreover, the water activity of the dried products was obtained to determine their susceptibility to spoilage. The measured values were below the maximum threshold set by the Philippine national standard for dried fish, which indicates that the dried products can be sold commercially. Lastly, the minimum viable product of the solar dryer developed in this study costs Php 20,000.00 (USD 350.00), making this an affordable alternative. © 2024 IEEE.

关键词： Solar dryers

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A Data-Centric Method to Identify Lung Cancer Using a Variety of Machine Learning Approaches

A Data-Centric Method to Identify Lung Cancer Using a Variet...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Istiack Amin Al-Amain Tashreef Muhammad Khandaker Mohammad Mohi Uddin Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Lung cancer continues to be the leading cause of cancer-related deaths worldwide, and early detection is essential to extending patient life expectancy. The goal of this study is to boost the precision of lung cancer diagnosis through the use of modern machine learning approaches. We developed a thorough data pretreatment strategy that included feature selection, encoding, oversampling, and hyper parameter tuning to handle data discrepancies. We used the Kaggle dataset to assess six ML algorithms: Naive Bayes, Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. With an accuracy of 99.38%, precision of 99.39%, recall of 99.38%, and F1 score of 99.38%, the SVM approach performed exceptionally well.

关键词： Support vector machines Machine learning algorithms Accuracy Computational modeling Lung cancer Predictive models Nearest neighbor methods Feature extraction Data models Encoding

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Optimization of Double Wishbone Suspension: Evaluating the Performance of Classes of Algorithms

Optimization of Double Wishbone Suspension: Evaluating the P...

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International Conference on Applied Mathematics & computer science (ICAMCS)

作者： Muhammad Waqas Arshad Stefano Lodi Dept of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798350363807

ISBN: (纸本)9798350363814

Double wishbone suspension systems are employed in high-performance vehicles owing to their superior control over wheel motion. The design of a double-wishbone suspension is a challenging multi-objective problem due to the many inter- related quantities that are key in determining its performance and vary during its motion. The design activity is thus a time-consuming and complex iterative process that aims to tune their variation and minimize deviations from the desired performance characteristics while requiring the satisfaction of mechanical constraints. Computational optimization methods can be integrated into the design activity only after an assessment of their capability to support efficiently and effectively an iterative workflow. In this work, we start with the mathematical modeling of the camber angle of the double wishbone suspension. Then, we use the multi-model optimization approach that includes metaheuristic algorithms (Genetic Algorithms, Particle Swarm Optimization, Gradient Descent, and Ant Colony Optimization) and classical methods (Nelder-Mead, Interior Point, Simulated Annealing, and Random Search). These methods are assessed in terms of objective value, computational time, and memory usage in an effort to minimize the variation of camber angles from the specified constraints. The results prove that the Interior Point method is more accurate and efficient in optimizing the suspension's camber angle across its operational range, followed by other classical methods.

关键词： Ant colony optimization Three-dimensional displays Computational modeling Wheels Simulated annealing Mathematical models Computational efficiency Iterative methods Particle swarm optimization Genetic algorithms

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