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Fire and Smoke Detection Method in Diverse Scenarios Based on YOLOv8-FD

Fire and Smoke Detection Method in Diverse Scenarios Based o...

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Artificial Intelligence and Computer Engineering (ICAICE), International conference on

作者： Ziyu Wei Xiaoya Liu Yaru Qiu Yuanyuan Sun School of Data Science Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Targeting the challenges of insufficient accuracy, missed detections, false detections, and single scene in existing fire smoke detection, an enhanced YOLOv8 fire smoke detection method is suggested in diversified scenarios. the YOLOv8-FD model was proposed. By designing EfficientViT as the backbone network to strengthen its local feature extraction capability while maintaining the detection model's power to extract global features, more comprehensive feature content of the target of interest is obtained, solving the problem of unclear flame boundary features and efficiently improving the situation of lost and fake detections, thereby enhancing detection accuracy. A C2f_EMSC group convolution structure has been designed, which can balance the increase in parameter and computational complexity attributable to deep convolution in the Neck part of the model, and ensure more comprehensive and efficient extraction of image feature information at different levels, effectively improving the problem of long real-time response time to fires. At the same time, coupling the LSKA attention mechanism with the SPPF structure to some extent optimizes the detection difficulty problem caused by small targets in the early stage of flames, reduces computational complexity, and improves detection efficiency. Finally, the detection capability of the YOLOv8-FD pattern was validated through experiments. the experimental findings indicate that the precision of this detection approach reaches 94.2%, the mAP0.5 index reaches 95%, and the FPS can be stable at 130FPS, which can ensure the real-time recognition of fire while ensuring the accuracy of identification and reducing the occurrence of false alarms and missed positives.

关键词： Accuracy Convolution Computational modeling Fires Feature extraction Real-time systems Inference algorithms Neck Time factors Computational complexity

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Alleviating the Naive Bayes Assumption using Filter Approaches

Alleviating the Naive Bayes Assumption using Filter Approach...

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International conference on Smart Systems and Inventive Technology (ICSSIT)

作者： R. Siva Subramanian P. Girija K. Sudha J. Aswini S. SivaKumar N. V. S. Nattesan Dept of CSE S.A.Engineering College Dept of CSBS R.M.D Engineering College Dept of AI & ML Saveetha Engineering College Autonomous Affiflated to Anna University

Particularly compared to other machine learning algorithms, the Naive Bayes algorithm is notable for its widespread application in a wide range of classification problems despite its seeming simplicity. However, the conditional assumptions it makes about the dataset are largely invalidated by the real-time data. As a result of this, the performance of the prediction is less than optimal. To solve the problem, a multitude of researchers came up with a variety of methodologies to alleviate the NB assumption. 1. Structure Learning, 2. Feature Selection, 3. Data Expansion, and 4. Attribute Weighting is the headings for the different strategies that have been proposed to mitigate the effects of the NB assumption. In comparison to the other methods, feature selection has garnered significantly more attention in recent years. Since the intention of feature selection is to find the relevant subset of variables from the complete dataset in order to improve the NB prediction and fulfill the NB presumption, it is crucial that this is accomplished without diminishing the amount of information known about the dataset. the use of feature selection in conjunction with NB is implemented in this work in order to alleviate its conditional assumption. there are several sorts of feature engineering approaches seen in nature, and employ the filter FS technique in the proposed research. Because this filter FS works quickly and effectively in comparison to other FS methodologies. In this work, two distinct Filter FS are applied, and the features that are recorded are then modeled using the NB algorithm. the experimental technique is carried out from two distinct points of view: the first perspective uses Filter FS with NB, while the second perspective uses NB only. the outcomes obtained from the two methods are evaluated, compared, and forecasted with the help of a variety of validity scores. According to the conclusion of the research, Filter-NB performs much better than NB. Also, the ti

关键词： Machine learning algorithms Filtering algorithms Feature extraction Information filters Real-time systems Naive Bayes methods Time complexity

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Classification and Analysis of Distributed Denial of Service Attacks using Machine Learning Techniques

Classification and Analysis of Distributed Denial of Service...

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Advances in Science and Technology (ICAST), International conference on

作者： P.S. Nandhini S. Kuppuswami C. Abinaya R.B. Boomika N. Deepthi Department of CSE Kongu Engineering college Erode Department of CSE VCEW Namakkal India Department of CSE KEC Erode India

ISBN: (纸本)9781665492645

According to the recent statistics, DDoS attacks are responsible for the majority of overall network attacks. A distributed denial of service attack, sometimes known as a DDoS attack will destroy the server system resources such as the CPU, control and memory. As a result, the server will be too busy responding to a DDoS attack to provide service to the genuine users. Networks struggle to discern between malicious and lawful transmissions. Due to numerous variables like the rigidity, complexity and networking equipment, testing and deploying DDoS methods is difficult. Machine Learning are used to detect the DDoS attack but the best ML model to classify the DDoS attack is yet to be found. the proposed system makes use of supervised learning algorithm to find out the model that best classifies the DDoS attacks. Performance metrices have been calculated for various models. To ensure its suitability, the result was also compared with those from other models. As a result, accuracy of more than 96 percent have been achieved using Random Forest algorithm.

关键词： Machine learning algorithms Supervised learning Denial-of-service attack Classification algorithms Rigidity complexity theory Servers

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Detection of S700K Switch Machine Positions Based on an Improved SSD Model

Detection of S700K Switch Machine Positions Based on an Impr...

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Artificial Intelligence and Electromechanical Automation (AIEA), International conference on

作者： Rui Zhu Chun Li School of Railway Transportation Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

the current method of detecting the locating position and reverse position of the S700K switch machine is susceptible to human operation error. therefore, image-based object detection technology has become a solution. To address issues such as large model size, low detection accuracy and difficulty in deployment on resource-limited devices in current object detection algorithms, a detection method for the locating position and reverse position of 5700 K switch machine based on an improved single shot multibox detector (SSD) model is proposed. the VGG-16 network is replaced by MobileNetV2 and inverted residual blocks are used to replace four additional feature layers in the SSD model to reduce the model size. An improved receptive field block (RFB) is introduced in the shallow layers to expand the receptive field and enhance feature representation capabilities. A feature fusion module is designed to further enhance the learning ability of the targets. the results demonstrate that the improved SSD model outperforms SSD, Faster R-CNN, YOLO v4-tiny, and YOLO v7 in overall performance, with a mean average precision (mAP) of 95.51%, a model size of 15.7 MB, and a detection speed of 38.79 frames per second.

关键词： YOLO Performance evaluation Accuracy Automation Switches Voltage Detectors Feature extraction complexity theory Relays

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CfRNet: A Lightweight Convolutional Neural Network Classification Model for Rock Image

CfRNet: A Lightweight Convolutional Neural Network Classific...

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International conference on Computer Engineering and Applications (ICCEA)

作者： Liuyi Tao Xiaochuan Li Jiaqi Li Jinyi Wang College of Computer and Network Security Chengdu University of Technology Chengdu China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In the realm of geological research, the precise identification and classification of lithological formations stand as pivotal elements for comprehensive analysis and interpretation of geological structures. this is particularly crucial for tasks such as reservoir characterization and the exploration of solid metal mineral resources. However, existing methodologies encounter challenges characterized by inefficiencies, inaccuracies, and superfluous parameters within lithological image classification algorithms. To address these issues, this study presents a pioneering approach: the CfRNet, a specialized lightweight convolutional neural network tailored explicitly for rock images. By integrating the innovative VanillaNet module, resource utilization efficiency is notably improved, while the incorporation of the GhostNet module effectively mitigates redundancies in feature information, thereby amplifying feature extraction and perception capabilities. As a result, the CfRNet model demonstrates significantly enhanced performance in classification and detection tasks. Experimental validation showcases an exceptional recognition accuracy of 93.6%, highlighting not only its superior precision but also its commendable generalization capability and robustness. the CfRNet model thus emerges as a promising avenue for advancing lithological image classification methodologies, with far-reaching implications for geological analysis and resource exploration endeavors.

关键词： Training Analytical models Rocks Feature extraction Solids complexity theory Convolutional neural networks

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Spatially Variant based on Patch Division for Digital Pill Analysis Using Prototype Model

Spatially Variant based on Patch Division for Digital Pill A...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： R.K. Nehaa N. Bharathiraja Daniel Joseph Samuel T Deepa S. Hariprasad Biomedical Engineering department Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Tiruvallur India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Polenza Tech Solutions Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

the introduction of digital pills equipped with advanced multichannel sensors signifies revolutionary progress in remote healthcare monitoring. this study introduces a model of a smart pill that combines a valproate sensor with cutting-edge sensor technology and wireless communication capabilities. the digital pill enables real-time tracking and provides complete data insights through Zigbee and IoT apps. this information is accessible to patients and healthcare providers using machine learning *** a digital pill with vital medical sensors links the pharmaceutical, medical, and biomedical industries. the sensor’s primary goal is medication compliance, enhancing patient outcomes, drug management, and healthcare delivery through smooth ingestion. the valproate sensor, Arduino Uno, and wireless communication technology demonstrate how digital pills could revolutionize healthcare monitoring and delivery. this invention enables patient compliance, trustworthy health indicators, and successful healthcare management.

关键词： Wireless communication Data analysis Accuracy Zigbee Medical services Prediction algorithms Biomedical monitoring Time complexity Intelligent sensors Monitoring

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Data-driven Based Technique for Joint Power Allocation and User Pairing in NOMA System 9

Data-driven Based Technique for Joint Power Allocation and U...

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9th International Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2021

作者： Mohamed, Donia Magdy, Ahmed Elghandour, Osama Nasser, Ahmed Higher Institute of Engineering and Technology 5th Settlement University Department of Electronics and Communication Egypt Suez Canal University Faculty of Engineering Department of Electronics and Communication Egypt Helwan University Faculty of Engineering Department of Electronics and Communication Egypt

ISBN: (纸本)9781665482929

As one of the proposed multiple access techniques of the fifth-generation and beyond systems, NOMA, which stands for non-orthogonal multiple access. NOMA is a power domain access technique, which ensures better spectrum efficiency and system capacity. However, the representation of NOMA is challenging because of inter-user interference and the complexity of successive interference cancellation (SIC) in the receiver. We suggest a low-complexity power allocation (PA) and user pairing (UP) scheme using a model-free technique that depends on the real-time input and output calculations, data-driven (DD), where the near-optimal solution has been found. So, data-driven can be utilized as an optimization tool for finding a near-optimum solution of the original NP-hard problem of joint PA and UP. Analytical and simulation results referred that the proposed algorithm can enhance spectrum efficiency compared with the existing algorithms. © 2021 IEEE.

关键词： Spectrum efficiency

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the Role of AI in Software Test Automation

The Role of AI in Software Test Automation

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Advanced Research in Computing (ICARC), International conference on

作者： R.M.S. Ranapana W.M. J. I. Wijayanayake Department of Industrial Management University of Kelaniya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Artificial Intelligence (AI) has emerged as a transformative force in software test automation, significantly enhancing the efficiency, accuracy, and reliability of testing processes. this research will investigates the role of AI in software test automation, focusing on key methodologies, applications, and challenges encountered during implementation. the review identifies and analyzes various AI-driven techniques, including Machine Learning (ML), Neural Networks, and Genetic algorithms, which are utilized to optimize testing activities such as test case generation, defect detection, and test execution. Findings indicate that AI can substantially improve the software testing lifecycle by automating repetitive tasks, reducing human error, and increasing test coverage. By leveraging AI algorithms, organizations can achieve faster turnaround times and enhance the overall quality of software products. Additionally, AI facilitates predictive analytics, enabling teams to identify potential defects early in the development process, thereby minimizing costs and time associated with late-stage bug fixes. However, the resaerch also highlights several challenges that hinder the widespread adoption of AI in software testing. Issues such as data quality, model overfitting, and the complexities of integrating AI solutions with existing testing frameworks present significant barriers. Furthermore, many AI applications remain largely theoretical or limited to academic research, lacking real-world *** insights gained from this review are invaluable for both researchers and practitioners aiming to harness AI's capabilities to revolutionize software testing practices. By addressing the identified challenges and fostering collaboration between academia and industry, stakeholders can develop more robust frameworks and models that leverage AI's potential, ultimately creating a more efficient and effective software testing environment.

关键词： Software testing Automation Accuracy Reviews Data integrity Machine learning complexity theory Stakeholders Artificial intelligence Defect detection

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A Hybrid Deep-learning/Fingerprinting for Indoor Positioning Based on IEEE P802.11az

A Hybrid Deep-learning/Fingerprinting for Indoor Positioning...

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International conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Nader. G. Rihan Mahmoud Abdelaziz Samy S. Soliman University of Science and Technology Zewail City of Science Technology and Innovation Giza Egypt Electronics and Electrical Communications Engineering Department Faculty of Engineering Cairo University Giza Egypt

ISBN: (纸本)9781665482387

Many different technologies were proposed in the past few years for enhancing indoor positioning: WiFi, Radio Frequency Identification (RFID), Ultra Wide Band (UWB), and Bluetooth to mention some. this study followed the recent IEEE positioning standard (P802.11 az). the standard was developed to enhance indoor navigation by minimizing the consumption power with low hardware complexity. therefore, this standard enables the usage of artificial intelligence algorithms with relatively high complexity. Also, the usage of this standard will enhance indoor localization and positioning for different commercial purposes. We proposed two methods: Time Of Arrival (TOA) and fingerprinting-deep learning, considering a simple Single Input-Single Input (SISO) system at five Gigahertz with the highest standard allowable bandwidth. the behavior of TOA had very low performance considering a realistic multi-path case. On the other hand, the deep learning algorithm achieved ultra-high indoor positioning resolution (around twelve centimeters). Although TOA is a technique that relies on a simple hardware algorithm relative to deep learning, this paper proved the failure of TOA in a simple indoor environment even using the latest IEEE positioning standard compared with the deep learning method.

关键词： Deep learning Location awareness Signal processing algorithms Hardware complexity theory Standards IEEE 802.11 Standard

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Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

Towards Highly-Efficient k-Nearest Neighbor Algorithm for Bi...

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Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS), International conference on

作者： Hassan I. Abdalla Ali A. Amer College of Technological Innovation Zayed University Abu Dhabi U.A.E Computer Science Department TAIZ University TAIZ Yemen

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a test point in a feature space. A large number of works have been developed in the literature to accelerate the speed of data classification using kNN. In parallel with these works, we present a novel K-nearest neighbor variation with neighboring calculation property, called NCP-kNN. NCP-kNN comes to solve the search complexity of kNN as well as the issue of high-dimensional classification. In fact, these two problems cause an exponentially increasing level of complexity, particularly with big datasets and multiple k values. In NCP-kNN, every test point’s distance is checked with only a limited number of training points instead of the entire dataset. Experimental results on six small datasets, show that the performance of NCP-kNN is equivalent to that of standard kNN on small and big datasets, with NCP-kNN being highly efficient. Furthermore, surprisingly, results on big datasets demonstrate that NCP-kNN is not just faster than standard kNN but also significantly superior. the findings, on the whole, show that NCP-kNN is a promising technique as a highly-efficient kNN variation for big data classification.

关键词： Training Machine learning algorithms Text categorization Machine learning Big Data complexity theory Classification algorithms

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